Hypothesis selection and presentation of one or more advisories

ABSTRACT

A computationally implemented method includes, but is not limited to: selecting at least one hypothesis from a plurality of hypotheses relevant to a user, the selection of the at least one hypothesis being based, at least in part, on at least one reported event associated with the user; and presenting one or more advisories related to the hypothesis. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is related to and claims the benefit of theearliest available effective filing date(s) from the following listedapplication(s) (the “Related Applications”) (e.g., claims earliestavailable priority dates for other than provisional patent applicationsor claims benefits under 35 USC §119(e) for provisional patentapplications, for any and all parent, grandparent, great-grandparent,etc. applications of the Related Application(s)). All subject matter ofthe Related Applications and of any and all parent, grandparent,great-grandparent, etc. applications of the Related Applications isincorporated herein by reference to the extent such subject matter isnot inconsistent herewith.

RELATED APPLICATIONS

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/313,659, entitled CORRELATING SUBJECTIVE USERSTATES WITH OBJECTIVE OCCURRENCES ASSOCIATED WITH A USER, naming ShawnP. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, EricC. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D.Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L.Wood, Jr., as inventors, filed 21 Nov. 2008, now U.S. Pat. No.8,046,455.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/315,083, entitled CORRELATING SUBJECTIVE USERSTATES WITH OBJECTIVE OCCURRENCES ASSOCIATED WITH A USER, naming ShawnP. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, EricC. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D.Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L.Wood, Jr., as inventors, filed 26 Nov. 2008, now U.S. Pat. No.8,005,948.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/319,135, entitled CORRELATING DATA INDICATING ATLEAST ONE SUBJECTIVE USER STATE WITH DATA INDICATING AT LEAST ONEOBJECTIVE OCCURRENCE ASSOCIATED WITH A USER, naming Shawn P. Firminger;Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C. Leuthardt;Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D. Rinaldo, Jr.;Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood, Jr. asinventors, filed 31 Dec. 2008, now U.S. Pat. No. 7,937,465.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/319,134, entitled CORRELATING DATA INDICATING ATLEAST ONE SUBJECTIVE USER STATE WITH DATA INDICATING AT LEAST ONEOBJECTIVE OCCURRENCE ASSOCIATED WITH A USER, naming Shawn P. Firminger;Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C. Leuthardt;Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D. Rinaldo, Jr.;Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood, Jr. asinventors, filed 31 Dec. 2008, now U.S. Pat. No. 7,945,632.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/378,162, entitled SOLICITING DATA INDICATING ATLEAST ONE OBJECTIVE OCCURRENCE IN RESPONSE TO ACQUISITION OF DATAINDICATING AT LEAST ONE SUBJECTIVE USER STATE, naming Shawn P.Firminger; Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C.Leuthardt; Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D.Rinaldo, Jr.; Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood,Jr. as inventors, filed 9 Feb. 2009, now U.S. Pat. No. 8,028,063.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/378,288, entitled SOLICITING DATA INDICATING ATLEAST ONE OBJECTIVE OCCURRENCE IN RESPONSE TO ACQUISITION OF DATAINDICATING AT LEAST ONE SUBJECTIVE USER STATE, naming Shawn P.Firminger; Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C.Leuthardt; Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D.Rinaldo, Jr.; Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood,Jr. as inventors, filed 11 Feb. 2009, now U.S. Pat. No. 8,032,628.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/380,409, entitled SOLICITING DATA INDICATING ATLEAST ONE SUBJECTIVE USER STATE IN RESPONSE TO ACQUISITION OF DATAINDICATING AT LEAST ONE OBJECTIVE OCCURRENCE, naming Shawn P. Firminger;Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C. Leuthardt;Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D. Rinaldo, Jr.;Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood, Jr. asinventors, filed 25 Feb. 2009, now U.S. Pat. No. 8,010,662.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/380,573, entitled SOLICITING DATA INDICATING ATLEAST ONE SUBJECTIVE USER STATE IN RESPONSE TO ACQUISITION OF DATAINDICATING AT LEAST ONE OBJECTIVE OCCURRENCE, naming Shawn P. Firminger;Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C. Leuthardt;Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D. Rinaldo, Jr.;Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood, Jr. asinventors, filed 26 Feb. 2009, which is currently co-pending, or is anapplication of which a currently co-pending application is entitled tothe benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/383,581, entitled CORRELATING DATA INDICATINGSUBJECTIVE USER STATES ASSOCIATED WITH MULTIPLE USERS WITH DATAINDICATING OBJECTIVE OCCURRENCES, naming Shawn P. Firminger, JasonGarms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, RoyceA. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr.,Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., asinventors, filed 24 Mar. 2009, which is currently co-pending, or is anapplication of which a currently co-pending application is entitled tothe benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/383,817, entitled CORRELATING DATA INDICATINGSUBJECTIVE USER STATES ASSOCIATED WITH MULTIPLE USERS WITH DATAINDICATING OBJECTIVE OCCURRENCES, naming Shawn P. Firminger, JasonGarms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, RoyceA. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr.,Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., asinventors, filed 25 Mar. 2009, now U.S. Pat. No. 8,010,663.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/384,660, entitled HYPOTHESIS BASED SOLICITATIONOF DATA INDICATING AT LEAST ONE SUBJECTIVE USER STATE, naming Shawn P.Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C.Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D.Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L.Wood, Jr., as inventors, filed 6 Apr. 2009, which is currentlyco-pending, or is an application of which a currently co-pendingapplication is entitled to the benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/384,779, entitled HYPOTHESIS BASED SOLICITATIONOF DATA INDICATING AT LEAST ONE SUBJECTIVE USER STATE, naming Shawn P.Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C.Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D.Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L.Wood, Jr., as inventors, filed 7 Apr. 2009, which is currentlyco-pending, or is an application of which a currently co-pendingapplication is entitled to the benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/387,487, entitled HYPOTHESIS BASED SOLICITATIONOF DATA INDICATING AT LEAST ONE OBJECTIVE OCCURRENCE, naming Shawn P.Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C.Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D.Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L.Wood, Jr., as inventors, filed 30 Apr. 2009, now U.S. Pat. No.8,086,668.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/387,465, entitled HYPOTHESIS BASED SOLICITATIONOF DATA INDICATING AT LEAST ONE OBJECTIVE OCCURRENCE, naming Shawn P.Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C.Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D.Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L.Wood, Jr., as inventors, filed 30 Apr. 2009, now U.S. Pat. No.8,103,613.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/455,309, entitled HYPOTHESIS DEVELOPMENT BASEDON SELECTIVE REPORTED EVENTS, naming Shawn P. Firminger, Jason Garms,Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A.Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., ClarenceT. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors,filed 28 May 2009, which is currently co-pending, or is an applicationof which a currently co-pending application is entitled to the benefitof the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/455,317, entitled HYPOTHESIS DEVELOPMENT BASEDON SELECTIVE REPORTED EVENTS, naming Shawn P. Firminger, Jason Garms,Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A.Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., ClarenceT. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors,filed 29 May 2009, which is currently co-pending, or is an applicationof which a currently co-pending application is entitled to the benefitof the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/456,249, entitled HYPOTHESIS SELECTION ANDPRESENTATION OF ONE OR MORE ADVISORIES, naming Shawn P. Firminger, JasonGarms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, RoyceA. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr.,Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., asinventors, filed 12 Jun. 2009, which is currently co-pending, or is anapplication of which a currently co-pending application is entitled tothe benefit of the filing date.

The U.S. Patent Office (USPTO) has published a notice to the effect thatthe USPTO's computer programs require that patent applicants referenceboth a serial number and indicate whether an application is acontinuation or continuation-in-part. Stephen G. Kunin, Benefit ofPrior-Filed Application, USPTO Official Gazette Mar. 18, 2003, availableat http://www.uspto.gov/web/offices/com/sol/og/2003/week11/patbene.htm.The present Applicant Entity (hereinafter “Applicant”) has providedabove a specific reference to the application(s) from which priority isbeing claimed as recited by statute. Applicant understands that thestatute is unambiguous in its specific reference language and does notrequire either a serial number or any characterization, such as“continuation” or “continuation-in-part,” for claiming priority to U.S.patent applications. Notwithstanding the foregoing, Applicantunderstands that the USPTO's computer programs have certain data entryrequirements, and hence Applicant is designating the present applicationas a continuation-in-part of its parent applications as set forth above,but expressly points out that such designations are not to be construedin any way as any type of commentary and/or admission as to whether ornot the present application contains any new matter in addition to thematter of its parent application(s).

All subject matter of the Related Applications and of any and allparent, grandparent, great-grandparent, etc. applications of the RelatedApplications is incorporated herein by reference to the extent suchsubject matter is not inconsistent herewith.

SUMMARY

A computationally implemented method includes, but is not limited toselecting at least one hypothesis from a plurality of hypothesesrelevant to a user, the selection of the at least one hypothesis beingbased, at least in part, on at least one reported event associated withthe user; and presenting one or more advisories related to thehypothesis. In addition to the foregoing, other method aspects aredescribed in the claims, drawings, and text forming a part of thepresent disclosure.

In one or more various aspects, related systems include but are notlimited to circuitry and/or programming for effecting theherein-referenced method aspects; the circuitry and/or programming canbe virtually any combination of hardware, software, and/or firmwareconfigured to effect the herein-referenced method aspects depending uponthe design choices of the system designer.

A computationally implemented system includes, but is not limited to:means for selecting at least one hypothesis from a plurality ofhypotheses relevant to a user, the selection of the at least onehypothesis being based, at least in part, on at least one reported eventassociated with the user; and means for presenting one or moreadvisories related to the hypothesis. In addition to the foregoing,other system aspects are described in the claims, drawings, and textforming a part of the present disclosure.

A computationally implemented system includes, but is not limited to:circuitry for selecting at least one hypothesis from a plurality ofhypotheses relevant to a user, the selection of the at least onehypothesis being based, at least in part, on at least one reported eventassociated with the user; and circuitry for presenting one or moreadvisories related to the hypothesis. In addition to the foregoing,other system aspects are described in the claims, drawings, and textforming a part of the present disclosure.

A computer program product including a signal-bearing medium bearing oneor more instructions selecting at least one hypothesis from a pluralityof hypotheses relevant to a user, the selection of the at least onehypothesis being based, at least in part, on at least one reported eventassociated with the user; and one or more instructions for presentingone or more advisories related to the hypothesis. In addition to theforegoing, other computer program product aspects are described in theclaims, drawings, and text forming a part of the present disclosure.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the drawings and the followingdetailed description.

BRIEF DESCRIPTION OF THE FIGURES

FIGS. 1 a and 1 b show a high-level block diagram a computing device 10operating in a network environment.

FIG. 2 a shows another perspective of the events data acquisition module102 of the computing device 10 of FIG. 1 b.

FIG. 2 b shows another perspective of the hypothesis selection module104 of the computing device 10 of FIG. 1 b.

FIG. 2 c shows another perspective of the presentation module 106 of thecomputing device 10 of FIG. 1 b.

FIG. 3 is a high-level logic flowchart of a process.

FIG. 4 a is a high-level logic flowchart of a process depictingalternate implementations of the hypothesis selection operation 302 ofFIG. 3.

FIG. 4 b is a high-level logic flowchart of a process depictingalternate implementations of the hypothesis selection operation 302 ofFIG. 3.

FIG. 4 c is a high-level logic flowchart of a process depictingalternate implementations of the hypothesis selection operation 302 ofFIG. 3.

FIG. 4 d is a high-level logic flowchart of a process depictingalternate implementations of the hypothesis selection operation 302 ofFIG. 3.

FIG. 4 e is a high-level logic flowchart of a process depictingalternate implementations of the hypothesis selection operation 302 ofFIG. 3.

FIG. 4 f is a high-level logic flowchart of a process depictingalternate implementations of the hypothesis selection operation 302 ofFIG. 3.

FIG. 4 g is a high-level logic flowchart of a process depictingalternate implementations of the hypothesis selection operation 302 ofFIG. 3.

FIG. 4 h is a high-level logic flowchart of a process depictingalternate implementations of the hypothesis selection operation 302 ofFIG. 3.

FIG. 4 i is a high-level logic flowchart of a process depictingalternate implementations of the hypothesis selection operation 302 ofFIG. 3.

FIG. 5 a is a high-level logic flowchart of a process depictingalternate implementations of the advisory presentation operation 304 ofFIG. 3.

FIG. 5 b is a high-level logic flowchart of a process depictingalternate implementations of the advisory presentation operation 304 ofFIG. 3.

FIG. 5 c is a high-level logic flowchart of a process depictingalternate implementations of the advisory presentation operation 304 ofFIG. 3.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof. In the drawings,similar symbols typically identify similar components, unless contextdictates otherwise. The illustrative embodiments described in thedetailed description, drawings, and claims are not meant to be limiting.Other embodiments may be utilized, and other changes may be made,without departing from the spirit or scope of the subject matterpresented here.

A recent trend that is becoming increasingly popular in thecomputing/communication field is to electronically record one'sfeelings, thoughts, and other aspects of the person's everyday life ontoan open diary. One place where such open diaries are maintained are atsocial networking sites commonly known as “blogs” where users may reportor post their latest status, personal activities, and various otheraspects of the users' everyday life. The process of reporting or postingblog entries is commonly referred to as blogging. Other socialnetworking sites may allow users to update their personal informationvia, for example, social networking status reports in which a user mayreport or post for others to view their current status, activities,and/or other aspects of the user.

A more recent development in social networking is the introduction andexplosive growth of microblogs in which individuals or users (referredto as “microbloggers”) maintain open diaries at microblog websites(e.g., otherwise known as “twitters”) by continuously orsemi-continuously posting microblog entries. A microblog entry (e.g.,“tweet”) is typically a short text message that is usually not more than140 characters long. The microblog entries posted by a microblogger mayreport on any aspect of the microblogger's daily life. Typically, suchmicroblog entries will describe the various “events” associated with orare of interest to the microblogger that occurs during a course of atypical day. The microblog entries are often continuously posted duringthe course of a typical day, and thus, by the end of a normal day, asubstantial number of events may have been reported and posted.

Each of the reported events that may be posted through microblog entriesmay be categorized into one of at least three possible categories. Thefirst category of events that may be reported through microblog entriesare “objective occurrences” that may or may not be associated with themicroblogger. Objective occurrences that are associated with amicroblogger may be any characteristic, incident, happening, or anyother event that occurs with respect to the microblogger or are ofinterest to the microblogger that can be objectively reported by themicroblogger, a third party, or by a device. Such events would include,for example, intake of food, medicine, or nutraceutical, certainphysical characteristics of the microblogger such as blood sugar levelor blood pressure that can be objectively measured, activities of themicroblogger observable by others or by a device, activities of othersthat may or may not be of interest to the microblogger, external eventssuch as performance of the stock market (which the microblogger may havean interest in), performance of a favorite sports team, and so forth. Insome cases, objective occurrences may not be at least directlyassociated with a microblogger. Examples of such objective occurrencesinclude, for example, external events that may not be directly relatedto the microblogger such as the local weather, activities of others(e.g., spouse or boss) that may directly or indirectly affect themicroblogger, and so forth.

A second category of events that may be reported or posted throughmicroblog entries include “subjective user states” of the microblogger.Subjective user states of a microblogger may include any subjectivestate or status associated with the microblogger that can only betypically reported by the microblogger (e.g., generally cannot bedirectly reported by a third party or by a device). Such statesincluding, for example, the subjective mental state of the microblogger(e.g., happiness, sadness, anger, tension, state of alertness, state ofmental fatigue, jealousy, envy, and so forth), the subjective physicalstate of the microblogger (e.g., upset stomach, state of vision, stateof hearing, pain, and so forth), and the subjective overall state of themicroblogger (e.g., “good,” “bad,” state of overall wellness, overallfatigue, and so forth). Note that the term “subjective overall state” aswill be used herein refers to those subjective states that may not fitneatly into the other two categories of subjective user states describedabove (e.g., subjective mental states and subjective physical states).

A third category of events that may be reported or posted throughmicroblog entries include “subjective observations” made by themicroblogger. A subjective observation is similar to subjective userstates and may be any subjective opinion, thought, or evaluationrelating to any external incidence. Thus, the difference betweensubjective user states and subjective observations is that subjectiveuser states relates to self-described subjective descriptions of theuser states of one's self while subjective observations relates tosubjective descriptions or opinions regarding external events. Examplesof subjective observations include, for example, a microblogger'sperception about the subjective user state of another person (e.g., “heseems tired”), a microblogger's perception about another person'sactivities (e.g., “he drank too much yesterday”), a microblogger'sperception about an external event (e.g., “it was a nice day today”),and so forth. Although microblogs are being used to provide a wealth ofpersonal information, thus far they have been primarily limited to theiruse as a means for providing commentaries and for maintaining opendiaries.

In accordance with various embodiments, methods, systems, and computerprogram products are provided to, among other things, select ahypothesis from a plurality of hypotheses based on at least one reportedevent associated with a user, the selected hypothesis being a hypothesisthat may link together (e.g., correlate) a plurality of different typesof events (i.e., event types). In some embodiments, the selectedhypothesis (as well as, in some cases, the plurality of hypotheses) maybe relevant to the user. After making the selection, the methods,systems, and computer program products may present one or moreadvisories related to the selected hypothesis. The methods, systems, andcomputer program products may be employed in a variety of environmentsincluding, for example, social networking environments, blogging ormicroblogging environments, instant messaging (IM) environments, or anyother type of environment that allows a user to, for example, maintain adiary.

In various implementations, a “hypothesis,” as referred to herein, maydefine one or more relationships or links between different types ofevents (i.e., event types) including at least a first event type (e.g.,a type of event such as a particular type of subjective user state, forexample, an emotional state such as “happy”) and a second event type(e.g., another type of event such as particular type of objectiveoccurrence, for example, favorite sports team winning a game). In somecases, a hypothesis may be represented by an events pattern that mayindicate spatial or sequential relationships between different eventtypes (e.g., different types of events such as subjective user statesand objective occurrences). Note that for ease of explanation andillustration, the following description will describe a hypothesis asdefining, for example, the sequential or spatial relationship betweentwo different event types, a first event type and a second event type.However, those skilled in the art will recognize that such a hypothesiscould also identify the relationships between three or more event types(e.g., a first event type, a second event type, a third event type, andso forth).

In some embodiments, a hypothesis may, at least in part, be defined orrepresented by an events pattern that indicates or suggests a spatial ora sequential (e.g., time/temporal) relationship between different eventtypes. Such a hypothesis, in some cases, may also indicate the strengthor weakness of the link between the different event types. That is, thestrength or weakness (e.g., soundness) of the correlation betweendifferent event types may depend upon, for example, whether the eventspattern repeatedly occurs and/or whether a contrasting events patternhas occurred that may contradict the hypothesis and therefore, weakenthe hypothesis (e.g., an events pattern that indicates a person becomingtired after jogging for thirty minutes when a hypothesis suggests that aperson will be energized after jogging for thirty minutes).

As briefly described above, a hypothesis may be represented by an eventspattern that may indicate spatial or sequential (e.g., time or temporal)relationship or relationships between multiple event types. In someimplementations, a hypothesis may merely indicate temporal sequentialrelationships between multiple event types that indicate the temporalrelationships between multiple event types. In alternativeimplementations a hypothesis may indicate a more specific timerelationship between multiple event types. For example, a sequentialpattern may represent the specific pattern of events that occurs along atimeline that may indicate the specific time intervals between eventtypes. In still other implementations, a hypothesis may indicate thespatial (e.g., geographical) relationships between multiple event types.

In various embodiments, the development of a hypothesis may beparticularly useful to a user (e.g., a microblogger or a socialnetworking user) that the hypothesis may be associated with. That is, insome instances a hypothesis may be developed for a user that may assistthe user in modifying his/her future behavior, while in other instancessuch a hypothesis may simply alert or notify the user that a pattern ofevents are repeatedly occurring. In other situations, such a hypothesismay be useful to third parties such as advertisers in order to assistthe advertisers in developing a more targeted marketing scheme. In stillother situations, such a hypothesis may help in the treatment ofailments associated with the user.

One way to develop a hypothesis (e.g., creation of and/or furtherdevelopment of a hypothesis) is to determine a pattern of reportedevents that repeatedly occurs with respect to a particular user and/orto compare similar or dissimilar reported pattern of events that occurswith respect to a user. For example, if a user such as a microbloggerreports repeatedly that after each visit to a particular restaurant, theuser always has an upset stomach, then a hypothesis may be created anddeveloped that suggests that the user will get an upset stomach aftervisiting the particular restaurant. If, on the other hand, afterdeveloping such a hypothesis, the user reports that the last time he ateat the restaurant, he did not get an upset stomach, then such a reportmay result in the weakening of the hypothesis. Alternatively, if afterdeveloping such a hypothesis, the user reports that the last time he ateat the restaurant, he again got an upset stomach, then such a report mayresult in a confirmation of the soundness of the hypothesis. Note thatthe soundness of a hypothesis (e.g., strength or weakness of thehypothesis) may depend upon how much the historical data supports such ahypothesis.

Numerous hypotheses may be developed and may be associated with aparticular user. For example, in the case of a microblogger, given theamount of “events data” (and the large amounts of reported eventsindicated by the events data) that may be provided by the microbloggervia microblog entries, a large number of hypotheses associated with themicroblogger may eventually be developed based on the reported eventsindicated by the events data. Alternatively, hypotheses may also beprovided by one or more third party sources. For example, a number ofhypotheses may be provided by other users or by one or more networkservice providers.

Thus, in accordance with various embodiments, methods, systems, andcomputer program products are provided to, among other things, select ahypothesis from a plurality of hypotheses that may be associated with aparticular user (e.g., a microblogger), where the selected hypothesismay link or correlate a plurality of different types of events (i.e.,event types). After making the selection, the methods, systems, andcomputer program products may present one or more advisories related tothe selected hypothesis.

FIGS. 1 a and 1 b illustrate an example environment in accordance withvarious embodiments. In the illustrated environment, an exemplary system100 may include at least a computing device 10 (see FIG. 1 b). Thecomputing device 10, which may be a server (e.g., network server) or astandalone device, may be employed in order to, among other things,acquire events data 60* that may indicate one or more reported events.For example, the events data 60* to be acquired may include dataindicating at least one reported event 61*, data indicating at least asecond reported event 62*, and so forth. Based on the one or morereported events indicated by the acquired events data 60*, the computingdevice 10 may then be configured to select at least one hypothesis 81*from a plurality of hypotheses 80. After selecting the at least onehypothesis 81*, the computing device 10 may be configured to present oneor more advisories 90 related to the at least one hypothesis 81*.

As indicated earlier, in some embodiments, the computing device 10 maybe a server while in other embodiments the computing device 10 may be astandalone device. In the case where the computing device 10 is anetwork server, the computing device 10 may communicate indirectly witha user 20 a via wireless and/or wired network 40. In contrast, inembodiments where the computing device 10 is a standalone device, it maycommunicate directly with a user 20 b via a user interface 122 (see FIG.1 b). In the following, “*” indicates a wildcard. Thus, references touser 20* may indicate a user 20 a or a user 20 b of FIGS. 1 a and 1 b.

In embodiments in which the computing device 10 is a network server, thecomputing device 10 may communicate with a user 20 a via a mobile device30 and through a wireless and/or wired network 40. A network server, aswill be described herein, may be in reference to a server located at asingle network site or located across multiple network sites or aconglomeration of servers located at multiple network sites. The mobiledevice 30 may be a variety of computing/communication devices including,for example, a cellular phone, a personal digital assistant (PDA), alaptop, a desktop, or other types of computing/communication devicesthat can communicate with the computing device 10. In some embodiments,the mobile device 30 may be a handheld device such as a cellulartelephone, a smartphone, a Mobile Internet Device (MID), an Ultra MobilePersonal Computer (UMPC), a convergent device such as a personal digitalassistant (PDA), and so forth.

In embodiments in which the computing device 10 is a standalonecomputing device 10 (or simply “standalone device”) that communicatesdirectly with a user 20 b, the computing device 10 may be any type ofportable device (e.g., a handheld device) or stationary device (e.g.,desktop computer or workstation). For these embodiments, the computingdevice 10 may be a variety of computing/communication devices including,for example, a cellular phone, a personal digital assistant (PDA), alaptop, a desktop, or other types of computing/communication devices. Insome embodiments, in which the computing device 10 is a handheld device,the computing device 10 may be a cellular telephone, a smartphone, anMID, an UMPC, a convergent device such as a PDA, and so forth. Invarious embodiments, the computing device 10 may be a peer-to-peernetwork component device. In some embodiments, the computing device 10and/or the mobile device 30 may operate via a Web 2.0 construct (e.g.,Web 2.0 application 268).

In various embodiments, the computing device 10 may be configured toacquire events data 60* from one or more sources. Events data 60*, aswill be described herein, may indicate the occurrences of one or morereported events. Each of the reported events indicated by the eventsdata 60* may or may not be associated with a user 20*. In someembodiments, a reported event may be associated with the user 20* if itis reported by the user 20* or it is related to some aspect about theuser 20* (e.g., the location of the user 20*, the local weather of theuser 20*, activities performed by the user 20*, physical characteristicsof the user 20* as detected by a sensor 35, subjective user state of theuser 20*, and so forth). At least three different types of reportedevents may be indicated by the events data 60*, subjective user statesassociated with a user 20*, objective occurrences, and subjectiveobservations made by the user 20* or by others (e.g., one or more thirdparties 50).

The events data 60* that may be acquired by the computing device 10 mayinclude at least data indicating at least one reported event 61* and/ordata indicating at least a second reported event 62*. Though notdepicted, the events data 60* may further include data indicatingincidences of a third reported event, a fourth reported event, and soforth (as indicated by the dots). The events data 60* including the dataindicating at least one reported event 61* and/or the data indicating atleast a second reported event 62* may be obtained from one or moredistinct sources (e.g., the original sources for the data). For example,in some implementations, a user 20* may provide at least a portion ofthe events data 60* (e.g., events data 60 a that may include the dataindicating at least one reported event 61 a and/or the data indicatingat least a second reported event 62 a).

In the same or different embodiments, one or more remote network devicesincluding one or more sensors 35 and/or one or more network servers 36may provide at least a portion of the events data 60* (e.g., events data60 b that may include the data indicating at least one reported event 61b and/or the data indicating at least a second reported event 62 b). Insame or different embodiments, one or more third party sources mayprovide at least a portion of the events data 60* (e.g., events data 60c that may include the data indicating at least one reported event 61 cand/or the data indicating at least a second reported event 62 c). Instill other embodiments, at least a portion of the events data 60* maybe retrieved from a memory 140 in the form of historical data. Thus, tosummarize, each of the data indicating at least one reported event 61*and the data indicating at least a second reported event 62* may beobtained from the same or different sources.

The one or more sensors 35 illustrated in FIG. 1 a may represent a widerange of devices that can monitor various aspects or events associatedwith a user 20 a (or user 20 b). For example, in some implementations,the one or more sensors 35 may include devices that can monitor theuser's physiological characteristics such as blood pressure sensors,heart rate monitors, glucometers, and so forth. In some implementations,the one or more sensors 35 may include devices that can monitoractivities of a user 20* such as a pedometer, a toilet monitoring system(e.g., to monitor bowel movements), exercise machine sensors, anaccelerometer to measure a person's movements which may indicatespecific activities, and so forth. The one or more sensors 35 may alsoinclude other types of sensor/monitoring devices such as video ordigital camera, global positioning system (GPS) to provide data that maybe related to a user 20* (e.g., locations of the user 20*), and soforth.

The one or more third parties 50 illustrated in FIG. 1 a may represent awide range of third parties and/or the network devices associated withsuch parties. Examples of third parties include, for example, otherusers (e.g., other microbloggers or other social networking site users),health care entities (e.g., dental or medical clinic, hospital,physician's office, medical lab, and so forth), content providers,businesses such as retail business, employers, athletic or socialgroups, educational entities such as colleges and universities, and soforth.

In brief, after acquiring the events data 60* including data indicatingat least one reported event 61* and/or data indicating at least a secondreported event 62* from one or more sources, the computing device 10 maybe designed to select at least one hypothesis 81* from a plurality ofhypotheses 80 based, at least in part, on at least one reported eventassociated with a user 20*. In some cases, the selected hypothesis 81*as well as the plurality of hypotheses 80 may be relevant to the user20*. In various embodiments, each of the plurality of hypotheses 80 mayhave been created and/or may have been at least initially provided(e.g., pre-installed) by a third party (e.g., network service providers,computing device manufacturer, and so forth) and/or may have beenfurther refined by the computing device 10.

After selecting the at least one hypothesis 81*, the computing device 10may be designed to execute one or more actions. One such action that maybe executed is to present one or more advisories 90 associated with theat least one hypothesis 81* that was selected. For example, thecomputing device 10 may present the one or more advisories 90 to a user20* (e.g., by transmitting the one or more advisories 90 to a user 20 aor indicating the one or more advisories 90 to a user 20 b via a userinterface 122), to one or more third parties 50, and/or to one or moreremote network devices (e.g., network servers 36). The one or moreadvisories 90 to be presented may include at least a presentation of theselected hypothesis 81*, an alert regarding past events related to thehypothesis 81* (e.g., past events that the hypothesis 81* may have beenbased on), a recommendation for a future action based on the selectedhypothesis 81*, a prediction of an occurrence of a future event based onthe selected hypothesis 81*, or other types of advisories.

As illustrated in FIG. 1 b, computing device 10 may include one or morecomponents and/or sub-modules. As those skilled in the art willrecognize, these components and sub-modules may be implemented byemploying hardware (e.g., in the form of circuitry such as applicationspecific integrated circuit or ASIC, field programmable gate array orFPGA, or other types of circuitry), software, a combination of bothhardware and software, or a general purpose computing device executinginstructions included in a signal-bearing medium. In variousembodiments, computing device 10 may include an events data acquisitionmodule 102, a hypothesis selection module 104, a presentation module106, a hypothesis development module 108, a network interface 120 (e.g.,network interface card or NIC), a user interface 122 (e.g., a displaymonitor, a touchscreen, a keypad or keyboard, a mouse, an audio systemincluding a microphone and/or speakers, an image capturing systemincluding digital and/or video camera, and/or other types of interfacedevices), one or more applications 126 (e.g., a web 2.0 application 268,one or more communication applications 267 including, for example, avoice recognition application, and/or other applications), and/or memory140, which may include a plurality of hypothesis 80. Note that althoughnot depicted, one or more copies of the one or more applications 126 maybe included in memory 140.

The events data acquisition module 102 may be configured to, among otherthings, acquire events data 60* from one or more distinct sources (e.g.,from a user 20*, from one or more third parties 50, from one or morenetwork devices such as one or more sensors 35 and/or one or morenetwork servers 36, from memory 140 and/or from other sources). Theevents data 60* to be acquired by the events data acquisition module 102may include one, or both, of data indicating at least one reported event61* and data indicating at least a second reported event 62*. Each ofthe data indicating at least one reported event 61* and the dataindicating at least a second reported event 62* may be acquired from thesame source or different sources. The events data acquisition module 102may also be designed to acquire additional data indicating a thirdreported event, a fourth reported event, and so forth. The events data60* may be acquired in the form of one or more electronic entries suchas blog (e.g., microblog) entries, status report entries, electronicmessage entries, diary entries, and so forth.

Referring now to FIG. 2 a illustrating particular implementations of theevents data acquisition module 102 of the computing device 10 of FIG. 1b. The events data acquisition module 102 may include a reception module202 for receiving events data 60* including at least one of the dataindicating at least one reported event 61* and the data indicating atleast a second reported event 62*. The reception module 202 may furtherinclude a user interface reception module 204 and/or a network interfacereception module 206. The user interface reception module 204 may beconfigured to receive, via a user interface 122, the events data 60*including at least one of the data indicating at least one reportedevent 61* and the data indicating at least a second reported event 62*.In contrast, the network interface reception module 206 may beconfigured to receive (e.g., via network interface 120) from a wirelessand/or wired network 40 the events data 60* including at least one ofthe data indicating at least one reported event 61* and the dataindicating at least a second reported event 62*. The reception module202 may be designed to receive the events data 60* including the dataindicating at least one reported event 61* and/or the data indicating atleast a second reported event 62* in various forms and from varioussources. For example, the events data 60* may be in the form ofelectronic entries such as blog entries (e.g., microblog entries),status report entries, and electronic messages. In variousimplementations, such entries may have originated from a user 20*, oneor more third parties 50*, or one or more remote network devices (e.g.,sensors 35 or network servers 36),

The hypothesis selection module 104 of the computing device 10 of FIG. 1b may be configured to, among other things, select a hypothesis 81* froma plurality of hypotheses 80 that may be relevant to a user 20*, theselection of the hypothesis 81* being based, at least in part, on atleast one reported event associated with the user 20* (e.g., at leastone reported event that is about or related to the user 20*, that mayhave been reported by the user 20*, or that may be of interest to theuser 20*). FIG. 2 b illustrates particular implementations of thehypothesis selection module 104 of FIG. 1 b. As illustrated, thehypothesis selection module 104 may include a reported event referencingmodule 208 and/or a comparison module 210 that may further include amatching module 212, a contrasting module 214, and/or a relationshipdetermination module 216 (that may further include a sequential linkdetermination module 218 and/or a spatial link determination module220). In various implementations, these sub-modules may be employed inorder to facilitate the hypothesis selection module 104 in selecting theat least one hypothesis 81*.

In brief, the reported event referencing module 208 may be designed toreference one or more reported events that may have been indicated bythe events data 60* acquired by the events data acquisition module 102.The referencing of the one or more reported events may facilitate thehypothesis selection module 104 in the selection of the at least onehypothesis 81*. In contrast, the comparison module 210 may be configuredto compare the at least one reported event (e.g., as referenced by thereported event referencing module 208) to one, or both, of at least afirst event type and a second event type that may be linked together bythe at least one hypothesis 81*.

The matching module 212 may be configured to determine whether the atleast one reported event at least substantially matches with the firstevent type and/or the second event type that may be indicated by the atleast one hypothesis 81*. On the other hand, the contrasting module 214may be configured to determine whether a second reported event (e.g., asindicated by the acquired events data 60*) is a contrasting event fromthe at least first event type and/or the second event type that may beindicated by the at least one hypothesis 81*.

The relationship determination module 216 may be configured to determinea relationship between a first reported event and a second reportedevent (e.g., as indicated by the acquired events data 60*). Thesequential link determination module 218 may facilitate the relationshipdetermination module 216 to determine a relationship between the firstreported event and the second reported event by determining a sequentiallink (e.g., a temporal or a more specific time relationship) between thefirst reported event and the second reported event. The spatial linkdetermination module 220 may facilitate the relationship determinationmodule 216 to determine a relationship between the first reported eventand the second reported event by determining a spatial link (e.g., ageographical relationship) between the first reported event and thesecond reported event.

FIG. 2 c illustrates particular implementations of the presentationmodule 106 of FIG. 1 b. In various implementations, the presentationmodule 106 may be configured to, among other things, present one or moreadvisories 90 related to the at least one hypothesis 81* selected by thehypothesis selection module 104. The presentation module 106, in variousimplementations, may include one or more sub-modules that may facilitatethe presentation of the one or more advisories 90. For example, and asillustrated, the presentation module 106 may include an indicationmodule 222 configured to indicate one or more advisories 90 related tothe at least one hypothesis 81* selected by the hypothesis selectionmodule 104. The presentation module 106 may also include a transmissionmodule 224 configured to transmit one or more advisories 90 related tothe at least one hypothesis 81* selected by the hypothesis selectionmodule 104 via, for example, at least one of a wireless network or awired network 40.

In various implementations, the presentation module 106 may include ahypothesis presentation module 226 configured to present (e.g., transmitvia a wireless and/or wired network 40 or indicate via a user interface122) at least one form of the at least one hypothesis 81* selected bythe hypothesis selection module 104. The at least one hypothesis 81* maybe presented in a number of different formats. For example, thehypothesis 81* may be presented in a graphical or iconic form, in audioform, or in textual form. Further, with respect to presenting the atleast one hypothesis 81* in textual form, the hypothesis 81* may bepresented in many different ways as there may be many different ways todescribe a hypothesis 81* (this is also true when the hypothesis 81* ispresented graphically or audioally). The hypothesis presentation module226, in various implementations, may further include an event typesrelationship presentation module 228 that is configured to present anindication of a relationship (e.g., spatial or sequential relationship)between at least a first event type and at least a second event type asreferenced by the at least one hypothesis 81* selected by the hypothesisselection module 104.

In various implementations, the event types relationship presentationmodule 228 may further include a soundness presentation module 230configured to present an indication of the soundness of the at leasthypothesis 81* selected by the hypothesis selection module 104. In someimplementations, the soundness presentation module 230 may furtherinclude a strength/weakness presentation module 232 configured topresent an indication of strength or weakness of correlation between theat least first event type and the at least second event type that may belinked together by the at least one hypothesis 81*, the at least onehypothesis 81* being selected by the hypothesis selection module 104.

The event types relationship presentation module 228, in variousalternative implementations, may include a time/temporal relationshippresentation module 234 that is configured to present an indication of atime or temporal relationship between the at least first event type andthe at least second event type linked together by the at least onehypothesis 81*. In some implementations, the event types relationshippresentation module 228 may be configured to present an indication of aspatial relationship between the at least first event type and the atleast second event type linked together by the at least one hypothesis81*.

In some implementations, the presentation module 106 may include aprediction presentation module 238 that is configured to present (e.g.,transmit via a wireless and/or wired network 40 or indicate via a userinterface 122) an advisory relating to a prediction of a future event.Such an advisory may be based on the at least one hypothesis 81*selected by the hypothesis selection module 104. For example, supposethe at least one hypothesis 81* suggests that there is a link betweenjogging and sore ankles, then upon the events data acquisition module102 acquiring data indicating that a user 20* went jogging, then thepredication presentation module 238 may present an indication that theuser 20* will subsequently have sore ankles.

In the same or different implementations, the presentation module 106may include a recommendation presentation module 240 that may beconfigured to present (e.g., transmit via a wireless and/or wirednetwork 40 or indicate via a user interface 122) a recommendation for afuture course of action. Such a recommendation may be based, at least inpart, on the at least one hypothesis 81* selected by the hypothesisselection module 104. For example, referring back to the abovejogging/sore ankle example, the recommendation presentation module 240may recommend that the user 20* ingest aspirin.

In some implementations, the recommendation presentation module 240 mayinclude a justification presentation module 242 that may be configuredto present a justification for the recommendation presented by therecommendation presentation module 240. For example, in the abovejogging/sore ankle example, the justification presentation module 242may present an indication that the user 20* should ingest the aspirinbecause her ankles will be sore as a result of jogging.

In various alternative implementations, the presentation module 106 mayinclude a past events presentation module 244 that may be configured topresent (e.g., transmit via a wireless and/or wired network 40 orindicate via a user interface 122) an indication of one or more pastevents. Such a presentation of past events may be based, at least inpart, on the at least one hypothesis 81* selected by the hypothesisselection module 104. For example, in the above jogging/sore ankleexample, the past events presentation module 244 may be designed topresent an indication that the user 20* in the past seems to always havesore ankles after going jogging.

In various implementations, the computing device 10 may include ahypothesis development module 108 that may be configured to develop oneor more hypothesis 81* (e.g., create new hypotheses or to further refinehypotheses). In various implementations, the development of a hypothesis81* may be based, at least in part, on events data 60* that indicate oneor more reported events. In some cases, the development of a hypothesis81* may be further based on historical data such as historical medicaldata, population data, past user data (e.g., past user data indicatingpast reported events associated with a user 20*), and so forth.

In various implementations, the computing device 10 of FIG. 1 b mayinclude one or more applications 126. The one or more applications 126may include, for example, one or more communication applications 267(e.g., text messaging application, instant messaging application, emailapplication, voice recognition system, and so forth) and/or Web 2.0application 268 to facilitate in communicating via, for example, theWorld Wide Web. In some implementations, copies of the one or moreapplications 126 may be stored in memory 140.

In various implementations, the computing device 10 may include anetwork interface 120, which may be a device designed to interface witha wireless and/or wired network 40. Examples of such devices include,for example, a network interface card (NIC) or other interface devicesor systems for communicating through at least one of a wireless networkor wired network 40. In some implementations, the computing device 10may include a user interface 122. The user interface 122 may compriseany device that may interface with a user 20 b. Examples of such devicesinclude, for example, a keyboard, a display monitor, a touchscreen, amicrophone, a speaker, an image capturing device such as a digital orvideo camera, a mouse, and so forth.

The computing device 10 may include a memory 140. The memory 140 mayinclude any type of volatile and/or non-volatile devices used to storedata. In various implementations, the memory 140 may include, forexample, a mass storage device, read only memory (ROM), programmableread only memory (PROM), erasable programmable read-only memory (EPROM),random access memory (RAM), flash memory, synchronous random accessmemory (SRAM), dynamic random access memory (DRAM), and/or other memorydevices. In various implementations, the memory 140 may store aplurality of hypotheses 80.

The various features and characteristics of the components, modules, andsub-modules of the computing device 10 presented thus far will bedescribed in greater detail with respect to the processes and operationsto be described herein.

FIG. 3 illustrates an operational flow 300 representing exampleoperations related to, among other things, hypothesis selection from aplurality of hypotheses and presentation of one or more advisories inresponse to the selection. In some embodiments, the operational flow 300may be executed by, for example, the computing device 10 of FIG. 1 b,which may be a server or a standalone device.

In FIG. 3 and in the following figures that include various examples ofoperational flows, discussions and explanations may be provided withrespect to the above-described exemplary environment of FIGS. 1 a and 1b, and/or with respect to other examples (e.g., as provided in FIGS. 2a-2 c) and contexts. However, it should be understood that theoperational flows may be executed in a number of other environments andcontexts, and/or in modified versions of FIGS. 1 a, 1 b, and 2 a-2 c.Also, although the various operational flows are presented in thesequence(s) illustrated, it should be understood that the variousoperations may be performed in different sequential orders other thanthose which are illustrated, or may be performed concurrently.

Further, in the following figures that depict various flow processes,various operations may be depicted in a box-within-a-box manner. Suchdepictions may indicate that an operation in an internal box maycomprise an optional example embodiment of the operational stepillustrated in one or more external boxes. However, it should beunderstood that internal box operations may be viewed as independentoperations separate from any associated external boxes and may beperformed in any sequence with respect to all other illustratedoperations, or may be performed concurrently.

In any event, after a start operation, the operational flow 300 may moveto a hypothesis selection operation 302 for selecting at least onehypothesis from a plurality of hypotheses relevant to a user, theselection of the at least one hypothesis being based, at least in part,on at least one reported event associated with the user. For instance,the hypothesis selection module 104 of the computing device 10 selectingat least one hypothesis 81* (e.g., a hypothesis that correlates or linksa first event type with a second event type) from a plurality ofhypotheses 80 relevant to a user 20* (e.g., hypotheses 80 that may bespecifically relevant to the user 20* or at least to a sub-group of thepopulation that the user 20* belongs to), the selection of the at leastone hypothesis 81* being based, at least in part, on at least onereported event associated with the user 20*. Note that in the followingdescription and for ease of illustration and understanding thehypothesis 81* to be selected through the hypothesis selection operation302 may be described as a hypothesis that links together or associatestwo types of events (i.e., event types). However, those skilled in theart will recognize that such a hypothesis 81* may actually relate to thelinking together of three or more types of events in various alternativeimplementations.

Next, operational flow 300 may include an advisory presentationoperation 304 for presenting one or more advisories related to thehypothesis. For instance, the presentation module 106 of the computingdevice 10 presenting (e.g., transmitting through a wireless and/or wirednetwork 40, or indicating via a user interface 122) one or moreadvisories 90 (e.g., an advisory relating to one or more past events, arecommendation for a future action, and so forth) related to thehypothesis 81*.

The at least one hypothesis 81* to be selected during the hypothesisselection operation 302 of FIG. 3 may be related to one or more types ofevents (i.e., event types) in various alternative implementations. Forexample, in some implementations, the hypothesis selection operation 302may include an operation 402 for selecting at least one hypothesis thatrelates to at least one subjective user state type as depicted in FIG. 4a. For instance, the hypothesis selection module 104 of the computingdevice 10 selecting at least one hypothesis 81* that relates to at leastone subjective user state type (e.g., a subjective mental state such asanger, a subjective user state such as upset stomach, or a subjectiveoverall state such as “good”).

In various implementations, the at least one hypothesis 81* to beselected through operation 402 may be directed to any one or more of anumber of different types of subjective user states. For example, insome implementations, operation 402 may include an operation 403 forselecting at least one hypothesis that relates to at least onesubjective mental state type as depicted in FIG. 4 a. For instance, thehypothesis selection module 104 of the computing device 10 selecting atleast one hypothesis 81* that relates to at least one subjective mentalstate type (e.g., anger, happiness, depression, alertness, nausea,jealousy, mental fatigue, and so forth).

In the same or different implementations, operation 402 may include anoperation 404 for selecting at least one hypothesis that relates to atleast one subjective physical state type as depicted in FIG. 4 a. Forinstance, the hypothesis selection module 104 of the computing device 10selecting at least one hypothesis 81* that relates to at least onesubjective physical state type (e.g., upset stomach, pain, blurryvision, cramps, and so forth).

In the same or different implementations, operation 402 may include anoperation 405 for selecting at least one hypothesis that relates to atleast one subjective overall state type as depicted in FIG. 4 a. Forinstance, the hypothesis selection module 104 of the computing device 10selecting at least one hypothesis 81* that relates to at least onesubjective overall state type (e.g., overall wellness, availability,unavailability or occupied, overall fatigue, and so forth).

In various implementations, the at least one hypothesis 81* to beselected through the hypothesis selection operation 302 may be relatedto at least one type of objective occurrence (i.e., objective occurrencetype). For example, in some implementations, the hypothesis selectionoperation 302 may include an operation 406 for selecting at least onehypothesis that relates to at least one objective occurrence type asdepicted in FIG. 4 a. For instance, the hypothesis selection module 104of the computing device 10 selecting at least one hypothesis 81* thatrelates to at least one objective occurrence type (e.g., user activity,external event, user geographical location, and so forth).

In various implementations, operation 406 may include one or moreadditional operations. For example, in some implementations, operation406 may include an operation 407 for selecting at least one hypothesisthat relates to at least a type of user activity as depicted in FIG. 4a. For instance, the hypothesis selection module 104 of the computingdevice 10 selecting at least one hypothesis 81* that relates to at leasta type of user activity (e.g., consumption of an edible item, a type ofsocial activity, a type of exercise activity, and so forth).

In some implementations, operation 407 may include an operation 408 forselecting at least one hypothesis that relates to at least a consumptionof an item as depicted in FIG. 4 a. For instance, the hypothesisselection module 104 of the computing device 10 selecting at least onehypothesis 81* that relates to at least a consumption of an item (e.g.,an edible item such as food, herbs, beverages, medicine, nutraceuticals,and so forth).

Operation 408, in turn, may further include one or more operations invarious alternative implementations. For example, in someimplementations, operation 408 may include an operation 409 forselecting at least one hypothesis that relates to at least a consumptionof a type of food item as depicted in FIG. 4 a. For instance, thehypothesis selection module 104 of the computing device 10 selecting atleast one hypothesis 81* that relates to at least a consumption of atype of food item (e.g., fruits, vegetables, meats, particular dishes,ethnic foods, alcoholic beverages, coffee, and so forth).

In the same or different implementations, operation 408 may include anoperation 410 for selecting at least one hypothesis that relates to atleast a consumption of a type of medicine as depicted in FIG. 4 a. Forinstance, the hypothesis selection module 104 of the computing device 10selecting at least one hypothesis 81* that relates to at least aconsumption of a type of medicine (e.g., pain killers such as aspirin oribuprofen, cold medication, alpha blockers, insulin, and so forth).

In the same or different implementations, operation 408 may include anoperation 411 for selecting at least one hypothesis that relates to atleast a consumption of a type of nutraceutical as depicted in FIG. 4 a.For instance, the hypothesis selection module 104 of the computingdevice 10 selecting at least one hypothesis 81* that relates to at leasta consumption of a type of nutraceutical (e.g., carrots, broccoli, redwine, green tea, and so forth).

In some implementations, operation 407 may include an operation 412 forselecting at least one hypothesis that relates to a type of exerciseactivity as depicted in FIG. 4 b. For instance, the hypothesis selectionmodule 104 of the computing device 10 selecting at least one hypothesis81* that relates to a type of exercise activity (e.g., working out on anexercise machine such as a treadmill or elliptical machine, jogging,lifting weights, aerobics, swimming, and so forth).

In some implementations, operation 407 may include an operation 413 forselecting at least one hypothesis that relates to a type of socialactivity as depicted in FIG. 4 b. For instance, the hypothesis selectionmodule 104 of the computing device 10 selecting at least one hypothesis81* that relates to a type of social activity (e.g., attending a party,dinner engagement with family and/or friends, playing with children,attending a play or movie with friends or family, playing golf withfriends, and so forth).

In some implementations, operation 407 may include an operation 414 forselecting at least one hypothesis that relates to a type of recreationalactivity as depicted in FIG. 4 b For instance, the hypothesis selectionmodule 104 of the computing device 10 selecting at least one hypothesis81* that relates to a type of recreational activity (e.g., playing golfor bowling, fishing, reading, watching television or movie, and soforth). Note that certain activities may belong to more than oneobjective occurrence type. For example, in the above, playing golf couldbe either a recreational activity or a social activity.

In some implementations, operation 407 may include an operation 415 forselecting at least one hypothesis that relates to a type of learning ortype of educational activity as depicted in FIG. 4 b. For instance, thehypothesis selection module 104 of the computing device 10 selecting atleast one hypothesis 81* that relates to a type of learning or type ofeducational activity (e.g., reading a book, attending a class orlecture, and so forth).

In various implementations, operation 406 of FIG. 4 a may include anoperation 416 for selecting at least one hypothesis that relates to oneor more types of activities performed by one or more third parties asdepicted in FIG. 4 b. For instance, the hypothesis selection module 104of the computing device 10 selecting at least one hypothesis 81* thatrelates to one or more types of activities performed by one or morethird parties 50 (e.g., a spouse or a boss going on a trip, childrenreturning home from college, in-laws visiting, and so forth).

In the same or different implementations, operation 406 may include anoperation 417 for selecting at least one hypothesis that relates to oneor more types of user physical characteristics as depicted in FIG. 4 b.For instance, the hypothesis selection module 104 of the computingdevice 10 selecting at least one hypothesis 81* that relates to one ormore types of user physical characteristics (e.g., blood pressure, bloodsugar level, heart rate, bacterial or viral infections, physicalinjuries, and so forth).

In the same or different implementations, operation 406 may include anoperation 418 for selecting at least one hypothesis that relates to oneor more types of external activities as depicted in FIG. 4 b. Forinstance, the hypothesis selection module 104 of the computing device 10selecting at least one hypothesis 81* that relates to one or more typesof external activities (e.g., weather, performance of sports team, stockmarket performance, and so forth).

In the same or different implementations, operation 406 may include anoperation 419 for selecting at least one hypothesis that relates to oneor more locations as depicted in FIG. 4 b. For instance, the hypothesisselection module 104 of the computing device 10 selecting at least onehypothesis 81* that relates to one or more locations (e.g., geographicallocations such as Hawaii or place of employment).

In various implementations, the hypothesis selection operation 302 mayinclude an operation 420 for selecting at least one hypothesis thatrelates to at least one subjective observation type as depicted in FIG.4 c. For instance, the hypothesis selection module 104 of the computingdevice 10 selecting at least one hypothesis 81* that relates to at leasta subjective observation type (e.g., subjective interpretation ofanother person's activities or of external events).

Operation 420, in turn, may further include one or more additionaloperations in various alternative implementations. For example, in someimplementations, operation 420 may include an operation 421 forselecting at least one hypothesis that relates to at least one type ofsubjective observation relating to a person as depicted in FIG. 4 c. Forinstance, the hypothesis selection module 104 of the computing device 10selecting at least one hypothesis 81* that relates to at least one typeof subjective observation relating to a person (e.g., a subjectiveinterpretation of another person's behavior or actions).

In some implementations, operation 421 may further include an operation422 for selecting at least one hypothesis that relates to at least onetype of subjective observation relating to a subjective user state ofthe person as depicted in FIG. 4 c. For instance, the hypothesisselection module 104 of the computing device 10 selecting at least onehypothesis 81* that relates to at least one type of subjectiveobservation relating to a subjective user state of the person (e.g.,subjective mental state such as anger). For example, one personobserving that a second person having a scowling expression andconcluding or observing that the second person is angry.

Operation 422, in turn, may include one or more additional operations.For example, in some implementations, operation 422 may include anoperation 423 for selecting at least one hypothesis that relates to atleast one type of subjective observation relating to a subjective mentalstate of the person as depicted in FIG. 4 c. For instance, thehypothesis selection module 104 of the computing device 10 selecting atleast one hypothesis 81* that relates to at least one type of subjectiveobservation relating to a subjective mental state of the person (e.g., asubjective observation made by a person about the alertness orinattentiveness of another person).

In the same or different implementations, operation 422 may include anoperation 424 for selecting at least one hypothesis that relates to atleast one type of subjective observation relating to a subjectivephysical state of the person as depicted in FIG. 4 c. For instance, thehypothesis selection module 104 of the computing device 10 selecting atleast one hypothesis 81* that relates to at least one type of subjectiveobservation relating to a subjective physical state of the person (e.g.,a subjective observation made by a person that another person is inpain).

In the same or different implementations, operation 422 may include anoperation 425 for selecting at least one hypothesis that relates to atleast one type of subjective observation relating to a subjectiveoverall state of the person as depicted in FIG. 4 c. For instance, thehypothesis selection module 104 of the computing device 10 selecting atleast one hypothesis 81* that relates to at least one type of subjectiveobservation relating to a subjective overall state of the person (e.g.,a subjective observation made by a person that another person appears tobe well).

In some implementations, operation 420 may include an operation 426 forselecting at least one hypothesis that relates to at least one type ofsubjective observation relating to a type of activity performed by aperson as depicted in FIG. 4 c. For instance, the hypothesis selectionmodule 104 of the computing device 10 selecting at least one hypothesis81* that relates to at least one type of subjective observation relatingto a type of activity performed by a person (e.g., subjectiveobservation made by a person of another person's work performance).

In some implementations, operation 420 may include an operation 427 forselecting at least one hypothesis that relates to at least one type ofsubjective observation relating to an occurrence of an external event asdepicted in FIG. 4 c. For instance, the hypothesis selection module 104of the computing device 10 selecting at least one hypothesis 81* thatrelates to at least one type of subjective observation relating to anoccurrence of an external event (e.g., a subjective observation of theperformance of the stock market).

Referring back to the hypothesis selection operation 302 of FIG. 3, invarious implementations the hypothesis selection operation 302 mayinclude an operation 428 for selecting from the plurality of hypothesesat least one hypothesis that links at least a first event type with atleast a second event type as depicted in FIG. 4 d. For instance, thehypothesis selection module 104 of the computing device 10 selectingfrom the plurality of hypotheses 80 at least one hypothesis 81* thatlinks at least a first event type (e.g., a subjective user state type,an objective occurrence type, or a subjective observation type) with atleast a second event type (e.g., a subjective user state type, anobjective occurrence type, or a subjective observation type). Note thatin various alternative implementations a hypothesis 81* may link twosimilar types of events such as two objective occurrences or twosubjective user states. For example, a hypothesis 81* that links theconsumption of rice with high blood sugar level, both of which areobjective occurrences. In another example, linking together the feelingof depression that occurs prior to feeling elation, both of which aresubjective user states.

Thus, in various implementations, operation 428 may involve selecting ahypothesis 81* that links similar or different types of events. Forexample, in some implementations, operation 428 may include an operation429 for selecting at least one hypothesis that links at least a firstsubjective user state type with at least a second subjective user statetype as depicted in FIG. 4 d. For instance, the hypothesis selectionmodule 104 of the computing device 10 selecting at least one hypothesis81* that links at least a first subjective user state type (e.g.,inattention or distracted) with at least a second subjective user statetype (e.g., anger). For example, such a hypothesis 81* may suggest thata person may be inattentive whenever the person is angry.

In some implementations, operation 428 may include an operation 430 forselecting at least one hypothesis that links at least one subjectiveuser state type with at least one objective occurrence type as depictedin FIG. 4 d. For instance, the hypothesis selection module 104 of thecomputing device 10 selecting at least one hypothesis 81* that links atleast one subjective user state type (e.g., subjective overall statesuch as “good”) with at least one objective occurrence type (e.g.,occurrence of an external event such as favorite sports team winning).For example, such a hypothesis 81* may suggest that a person may feelgood when his/her favorite sports team wins.

In some implementations, operation 428 may include an operation 431 forselecting at least one hypothesis that links at least one subjectiveuser state type with at least one subjective observation type asdepicted in FIG. 4 d. For instance, the hypothesis selection module 104of the computing device 10 selecting at least one hypothesis 81* thatlinks at least one subjective user state type (e.g., fatigued) with atleast one subjective observation type (e.g., subjective observation ofanger). For example, such a hypothesis 81* may suggest that a personwhen fatigued may appear to be angry by others.

In some implementations, operation 428 may include an operation 432 forselecting at least one hypothesis that links at least a first objectiveoccurrence type with at least a second objective occurrence type asdepicted in FIG. 4 d. For instance, the hypothesis selection module 104of the computing device 10 selecting at least one hypothesis 81* thatlinks at least a first objective occurrence type (e.g., stock marketcrash) with at least a second objective occurrence type (e.g., highblood pressure). For example, such a hypothesis 81* may suggest that aperson's blood pressure may elevate whenever the stock market crashes.

In some implementations, operation 428 may include an operation 433 forselecting at least one hypothesis that links at least one objectiveoccurrence type with at least one subjective observation type asdepicted in FIG. 4 d. For instance, the hypothesis selection module 104of the computing device 10 selecting at least one hypothesis 81* thatlinks at least one objective occurrence type (e.g., reduced bloodpressure) with at least one subjective observation type (e.g., happyboss). For example, such a hypothesis 81* may suggest that a person'sblood pressure may be reduced when the person observes that the person'sboss appears to be happy.

In some implementations, operation 428 may include an operation 434 forselecting at least one hypothesis that links at least a first subjectiveobservation type with at least a second subjective observation type asdepicted in FIG. 4 d. For instance, the hypothesis selection module 104of the computing device 10 selecting at least one hypothesis 81* thatlinks at least a first subjective observation type (e.g., happy spouse)with at least a second subjective observation type (e.g., nice weather).For example, such a hypothesis 81* may suggest that when a spousereports that the weather appears to be nice, the spouse may also appearto be happy as observed by the spouse's partner.

In some implementations, operation 428 may include an operation 435 forselecting at least one hypothesis that at least sequentially links atleast a first event type with at least a second event type as depictedin FIG. 4 d. For instance, the hypothesis selection module 104 of thecomputing device 10 selecting at least one hypothesis 81* that at leastsequentially links at least a first event type (e.g., eating spicyfoods) with at least a second event type (e.g., upset stomach). Forexample, such a hypothesis 81* may suggest that after eating spicyfoods, a person may develop a stomach ache.

In some implementations, operation 428 may include an operation 436 forselecting at least one hypothesis that at least spatially links at leasta first event type with at least a second event type as depicted in FIG.4 d. For instance, the hypothesis selection module 104 of the computingdevice 10 selecting at least one hypothesis 81* that at least spatiallylinks at least a first event type (e.g., depression) with at least asecond event type (happiness). For example, such a hypothesis 81* maysuggest that a person is happier in Hawaii than being in Los Angeles.

In various implementations, the at least one hypothesis 81* (as well as,in some cases, the plurality of hypotheses 80), may have been originallydeveloped based on historical data specifically associated with the user20* or on historical data specifically associated with at least asub-group of the general population that the user 20* belongs to. Forexample, in some implementations, the hypothesis selection operation 302of FIG. 3 may include an operation 437 for selecting at least onehypothesis that was developed based, at least in part, on historicaldata associated with the user as depicted in FIG. 4 e. For instance, thehypothesis selection module 104 of the computing device 10 selecting atleast one hypothesis 81* that was developed based, at least in part, onhistorical data (e.g., historical medical data associated with the user20*, previously reported events data including data indicating patternsof past reported events associated with the user 20*, and so forth)associated with the user 20*.

In some implementations, operation 437 may further include an operation438 for selecting at least one hypothesis that was developed based, atleast in part, on a historical events pattern specifically associatedwith the user as depicted in FIG. 4 e. For instance, the hypothesisselection module 104 of the computing device 10 selecting at least onehypothesis 81* that was developed based, at least in part, on ahistorical events pattern (e.g., an events pattern that indicatesincreased relaxation following 30 minutes of exercise) specificallyassociated with the user 20*.

In various implementations, the hypothesis selection operation 302 ofFIG. 3 may include an operation 439 for selecting at least onehypothesis that was developed based, at least in part, on historicaldata associated with at least a sub-group of a population, the userbeing included in the sub-group as depicted in FIG. 4 e. For instance,the hypothesis selection module 104 of the computing device 10 selectingat least one hypothesis 81* that was developed based, at least in part,on historical data (e.g., medical data) associated with at least asub-group (e.g., a particular ethnic group) of a population, the user20* being included in the sub-group.

In some implementations, operation 439 may include an operation 440 forselecting at least one hypothesis that was developed based, at least inpart, on a historical events pattern associated with at least thesub-group of the population as depicted in FIG. 4 e. For instance, thehypothesis selection module 104 of the computing device 10 selecting atleast one hypothesis 81* that was developed based, at least in part, ona historical events pattern (e.g., an events pattern that indicates arelationship between diarrhea and consumption of dairy products)associated with at least the sub-group of the population.

In some implementations, the hypothesis selection operation 302 mayinclude an operation 441 for selecting at least one hypothesis from aplurality of hypotheses, the plurality of hypotheses being specificallyassociated with the user as depicted in FIG. 4 e. For instance, thehypothesis selection module 104 of the computing device 10 selecting atleast one hypothesis 81* from a plurality of hypotheses 80, theplurality of hypotheses 80 being specifically associated with the user20*. For example, each of the plurality of hypothesis 80 may have beendeveloped based on patterns of reported events associated with the user20*.

In various implementations, the hypothesis selection operation 302 mayinclude an operation 442 for selecting at least one hypothesis from aplurality of hypotheses, the plurality of hypotheses being specificallyassociated with at least a sub-group of a population, the user being amember of the sub-group as depicted in FIG. 4 e. For instance, thehypothesis selection module 104 of the computing device 10 selecting atleast one hypothesis 81* from a plurality of hypotheses 80, theplurality of hypotheses 80 being specifically associated with at least asub-group of a population, the user 20* being a member of the sub-group.For example, each of the plurality of hypotheses 80 may have beendeveloped based on patterns of reported events associated with at leasta sub-group (e.g., gender or age group) of the general population.

The selection of the at least one hypothesis 81* in the hypothesisselection operation 302 of FIG. 3 may be based on a reported event thatmay have been reported through a variety of reporting methods. Forexample, in various implementations, the hypothesis selection operation302 may include an operation 443 for selecting at least one hypothesisfrom the plurality of hypotheses based, at least in part, on at leastone reported event reported via one or more electronic entries asdepicted in FIG. 4 f. For instance, the hypothesis selection module 104of the computing device 10 selecting at least one hypothesis 81* fromthe plurality of hypotheses 80 based, at least in part, on at least onereported event (e.g., as referenced by the reported event referencingmodule 208 of the computing device 10) reported via one or moreelectronic entries (e.g., blog or microblog entries, status reportentries, diary entries, instant message entries, text messaging entries,and so forth)) as received by, for example, reception module 202.

In particular, operation 443 may include an operation 444 for selectingat least one hypothesis from the plurality of hypotheses based, at leastin part, on at least one reported event reported via one or more blogentries in various implementations and as depicted in FIG. 4 f. Forinstance, the hypothesis selection module 104 of the computing device 10selecting at least one hypothesis 81* from the plurality of hypotheses80 based, at least in part, on at least one reported event reported viaone or more blog entries (e.g., microblog entries as provided by theuser 20* or by one or more third parties 50 such as other users) asreceived by, for example, reception module 202.

In some implementations, operation 443 may include an operation 445 forselecting at least one hypothesis from the plurality of hypothesesbased, at least in part, on at least one reported event reported via oneor more status reports as depicted in FIG. 4 f. For instance, thehypothesis selection module 104 of the computing device 10 selecting atleast one hypothesis 81* from the plurality of hypotheses 80 based, atleast in part, on at least one reported event reported via one or morestatus reports (e.g., as provided by the user 20* or by one or morethird parties 50 such as other users) as received by, for example,reception module 202.

In some implementations, operation 443 may include an operation 446 forselecting at least one hypothesis from the plurality of hypothesesbased, at least in part, on at least one reported event reported via oneor more electronic messages as depicted in FIG. 4 f. For instance, thehypothesis selection module 104 of the computing device 10 selecting atleast one hypothesis 81* from the plurality of hypotheses 80 based, atleast in part, on at least one reported event reported via one or moreelectronic messages such as email messages, text messages, IM messages,and so forth (e.g., as provided by the user 20* or by one or more thirdparties 50 such as other users) and as received by, for example,reception module 202.

In some implementations, operation 443 may include an operation 447 forselecting at least one hypothesis from the plurality of hypothesesbased, at least in part, on at least one reported event reported throughone or more electronic entries composed by the user as depicted in FIG.4 f. For instance, the hypothesis selection module 104 of the computingdevice 10 selecting at least one hypothesis 81* from the plurality ofhypotheses 80 based, at least in part, on at least one reported eventreported through one or more electronic entries (e.g., blog or microblogentries, status report entries, diary entries, instant message entries,text messaging entries, and so forth) composed by the user 20* and asreceived by, for example, reception module 202.

In some implementations, operation 443 may include an operation 448 forselecting at least one hypothesis from the plurality of hypothesesbased, at least in part, on at least one reported event reported throughone or more electronic entries composed by one or more third parties asdepicted in FIG. 4 f. For instance, the hypothesis selection module 104of the computing device 10 selecting at least one hypothesis 81* fromthe plurality of hypotheses 80 based, at least in part, on at least onereported event reported through one or more electronic entries (e.g.,blog or microblog entries, status report entries, diary entries, instantmessage entries, text messaging entries, and so forth) composed by oneor more third parties 50 and as received by, for example, receptionmodule 202.

In some implementations, operation 443 may include an operation 449 forselecting at least one hypothesis from the plurality of hypothesesbased, at least in part, on at least one reported event reported throughone or more electronic entries generated by one or more remote networkdevices as depicted in FIG. 4 f. For instance, the hypothesis selectionmodule 104 of the computing device 10 selecting at least one hypothesis81* from the plurality of hypotheses 80 based, at least in part, on atleast one reported event reported through one or more electronic entriesgenerated by one or more remote network devices (e.g., network servers,work stations, blood pressure monitors, glucometers, heart ratemonitors, GPS, exercise machine sensors, pedometer, accelerometer tomeasure user movements, toilet monitors to monitor toilet use, and soforth) and as received by, for example, reception module 202.

In some implementations, operation 449 may further include an operation450 for selecting at least one hypothesis from the plurality ofhypotheses based, at least in part, on at least one reported eventreported through one or more electronic entries generated by one or moresensors as depicted in FIG. 4 f. For instance, the hypothesis selectionmodule 104 of the computing device 10 selecting at least one hypothesis81* from the plurality of hypotheses 80 based, at least in part, on atleast one reported event reported through one or more electronic entriesgenerated by one or more sensors 35 (e.g., blood pressure monitors,glucometers, heart rate monitors, GPS, exercise machine sensors,pedometer, accelerometer to measure user movements, toilet monitors tomonitor toilet use, and so forth) and as received by, for example,reception module 202.

In various implementations, the hypothesis selection operation 302 ofFIG. 3 may make the selection of the at least one hypothesis 81* basedon a plurality of reported events. For example, in some implementations,the hypothesis selection operation 302 may include an operation 451 forselecting the at least one hypothesis from the plurality of hypothesesbased, at least in part, on at least the one reported event and a secondreported event as depicted in FIG. 4 g. For instance, the hypothesisselection module 104 of the computing device 10 selecting at least onehypothesis 81* from the plurality of hypotheses 80 based, at least inpart, on at least the one reported event (e.g., a subjective user state,an objective occurrence, or a subjective occurrence) and a secondreported event (e.g., a subjective user state, an objective occurrence,or a subjective occurrence).

In some implementations, operation 451 may include an operation 452 forselecting the at least one hypothesis from the plurality of hypothesesbased, at least in part, on at least one reported event of a first eventtype and a second reported event of a second event type as depicted inFIG. 4 g. For instance, the hypothesis selection module 104 of thecomputing device 10 selecting at least one hypothesis 81* from theplurality of hypotheses 80 based, at least in part, on at least onereported event of a first event type (e.g., subjective user state) and asecond reported event of a second event type (e.g., objectiveoccurrence).

In some implementations, operation 451 may include an operation 453 forselecting the at least one hypothesis from the plurality of hypothesesbased, at least in part, on at least one reported event that originatesfrom a first source and a second reported event that originates from asecond source as depicted in FIG. 4 g. For instance, the hypothesisselection module 104 of the computing device 10 selecting at least onehypothesis 81* from the plurality of hypotheses 80 based, at least inpart, on at least one reported event that originates from a first source(e.g., user 20*) and a second reported event that originates from asecond source (e.g., one or more sensors 35 or one or more third parties50).

Various approaches may be employed in the hypothesis selection operation302 of FIG. 3 in order to select the at least one hypothesis 81* fromthe plurality of hypotheses 80 based on the at least one reported event.For example, in some implementations, the hypothesis selection operation302 may include an operation 454 for selecting at least one hypothesisfrom a plurality of hypotheses based, at least in part, on a comparisonof the at least one reported event to one, or both, of a first eventtype and a second event type linked together by the at least onehypothesis as depicted in FIG. 4 g. For instance, the hypothesisselection module 104 of the computing device 10 selecting at least onehypothesis 81* from a plurality of hypotheses 80 based, at least inpart, on a comparison (e.g., as made by the comparison module 210) ofthe at least one reported event (e.g., reporting consumption ofalcoholic beverage) to one, or both, of a first event type (e.g.,feeling a hangover) and a second event type (e.g., consuming alcoholicbeverage) linked together by the at least one hypothesis 81*.

In some implementations, operation 454 may further include an operation455 for selecting the at least one hypothesis based, at least in part,on determining whether the at least one reported event at leastsubstantially matches with the first event type or the second event typeas depicted in FIG. 4 g. For instance, the hypothesis selection module104 of the computing device 10 selecting the at least one hypothesis 81*based, at least in part, on determining whether the at least onereported event (e.g., reporting a cloudy weather) at least substantiallymatches (e.g., as substantially matched by the matching module 212) withthe first event type (e.g., feeling melancholy) or the second event type(e.g., overcast weather).

In some implementations, operation 454 may include an operation 456 forselecting the at least one hypothesis based, at least in part, on acomparison of a second reported event to one, or both, of the firstevent type and the second event type as depicted in FIG. 4 g. Forinstance, the hypothesis selection module 104 of the computing device 10selecting the at least one hypothesis 81* based, at least in part, on acomparison (e.g., as compared by the comparison module 210) of a secondreported event (e.g., reporting a hangover) to one, or both, of thefirst event type (e.g., consuming alcoholic beverage) and the secondevent type (e.g., feeling a hangover).

In various implementations, operation 456 may further include anoperation 457 for selecting the at least one hypothesis based, at leastin part, on determining whether the second reported event at leastsubstantially matches with the first event type or the second event typeas depicted in FIG. 4 g. For instance, the hypothesis selection module104 of the computing device 10 selecting the at least one hypothesis 81*based, at least in part, on determining whether the second reportedevent (e.g., reporting feeling depressed) at least substantially matches(e.g., as substantially matched by the matching module 212) with thefirst event type (e.g., overcast weather) or the second event type(e.g., feeling melancholy).

In some implementations, operation 456 may include an operation 458 forselecting the at least one hypothesis based, at least in part, ondetermining whether the second reported event is a contrasting eventfrom the first event type or the second event type as depicted in FIG. 4g. For instance, the hypothesis selection module 104 of the computingdevice 10 selecting the at least one hypothesis 81* based, at least inpart, on determining whether the second reported event (e.g., reportingfeeling happy) is a contrasting event (e.g., as determined by thecontrasting module 214) from the first event type (e.g., overcastweather) or the second event type (e.g., feeling melancholy). Note thatsuch an operation may ultimately result in the assessment that the atleast one hypothesis 81* is not a sound or strong hypothesisparticularly as it relates to, for example, the user 20*.

In some implementations, operation 456 may include an operation 459 forselecting the at least one hypothesis based, at least in part, ondetermining a relationship between the first reported event and thesecond reported event as depicted in FIG. 4 h. For instance, thehypothesis selection module 104 of the computing device 10 selecting theat least one hypothesis 81* based, at least in part, on determining arelationship (e.g., the relationship determination module 216determining a sequential or spatial relationship) between the firstreported event (e.g., high blood sugar level) and the second reportedevent (e.g., consuming white rice).

Operation 459, in some implementations, may include an operation 460 forselecting the at least one hypothesis based, at least in part, ondetermining a sequential link between the first reported event and thesecond reported event as depicted in FIG. 4 h. For instance, thehypothesis selection module 104 of the computing device 10 selecting theat least one hypothesis 81* based, at least in part, on determining asequential link (e.g., the sequential link determination module 218determining a temporal relationship or a more specific timerelationship) between the first reported event and the second reportedevent.

In some implementations, operation 459 may include an operation 461 forselecting the at least one hypothesis based, at least in part, ondetermining a spatial link between the first reported event and thesecond reported event as depicted in FIG. 4 h. For instance, thehypothesis selection module 104 of the computing device 10 selecting theat least one hypothesis 81* based, at least in part, on determining aspatial link (e.g., as determined by the spatial link determinationmodule 220) between the first reported event and the second reportedevent.

In some implementations, operation 459 may include an operation 462 forselecting the at least one hypothesis based, at least in part, oncomparing the relationship between the first reported event and thesecond reported event to a relationship between the first event type andthe second event type of the at least one hypothesis as depicted in FIG.4 h. For instance, the hypothesis selection module 104 of the computingdevice 10 selecting the at least one hypothesis 81* based, at least inpart, on comparing (e.g., as compared by the comparison module 210) therelationship between the first reported event and the second reportedevent to a relationship between the first event type and the secondevent type of the at least one hypothesis 81*.

The hypothesis selection operation 302 of FIG. 3 may be executed invarious types of devices in various environments. For example, in someimplementations, the hypothesis selection operation 302 may include anoperation 463 for selecting the at least one hypothesis at a server asdepicted in FIG. 4 i. For instance, the hypothesis selection module 104of the computing device 10 selecting the at least one hypothesis 81*when the computing device 10 is a network server.

In other alternative implementations, the hypothesis selection operation302 may include an operation 464 for selecting the at least onehypothesis at a standalone device as depicted in FIG. 4 i. For instance,the hypothesis selection module 104 of the computing device 10 selectingthe at least one hypothesis 81* when the computing device 10 is astandalone device (e.g., a desktop computer, a laptop computer, aworkstation, or a handheld device such as a cellular telephone, asmartphone, a PDA, an MID, an UMPC, and so forth).

In some implementations, operation 464 may further include an operation465 for selecting the at least one hypothesis at a handheld device asdepicted in FIG. 4 i. For instance, the hypothesis selection module 104of the computing device 10 selecting the at least one hypothesis 81*when the computing device 10 is a handheld device (e.g., cellulartelephone, a smartphone, a PDA, an MID, an UMPC, and so forth).

In some implementations, the hypothesis selection operation 302 mayinclude an operation 466 for selecting the at least one hypothesis at apeer-to-peer network component device as depicted in FIG. 4 i. Forinstance, the hypothesis selection module 104 of the computing device 10selecting the at least one hypothesis 81* when the computing device 10is a peer-to-peer network component device.

In some implementations, the hypothesis selection operation 302 mayinclude an operation 467 for selecting the at least one hypothesis via aWeb 2.0 construct as depicted in FIG. 4 i. For instance, the hypothesisselection module 104 of the computing device 10 selecting the at leastone hypothesis 81* via a Web 2.0 construct (e.g., Web 2.0 application268).

Referring back to the operational flow 300 of FIG. 3, the advisorypresentation operation 304 of operational flow 300 may be executed in anumber different ways in various alternative implementations. Forexample, in some implementations, the advisory presentation operation304 may include an indication operation 502 for indicating the one ormore advisories related to the hypothesis via a user interface asdepicted in FIG. 5 a. For instance, the indication module 222 (see FIG.2 c) of the computing device 10 indicating the one or more advisoriesrelated to the hypothesis 81* via a user interface 122 (e.g., a displaymonitor such as a liquid crystal display, a touch screen, an audiosystem including one or more speakers, and/or other interface devices).

In various implementations, the advisory presentation operation 304 mayinclude a transmission operation 504 for transmitting the one or moreadvisories related to the hypothesis via at least one of a wirelessnetwork or a wired network as depicted in FIG. 5 a. For instance, thetransmission module 224 (see FIG. 2 c) of the computing device 10transmitting the one or more advisories 90 (e.g., a recommendation for afuture action based on the hypothesis 81* or an alert regarding thehypothesis 81*) related to the hypothesis 81* via at least one of awireless network or a wired network 40. In some cases, the computingdevice 10 may employ a network interface 120 in order to transmit theone or more advisories 90.

In some implementations, the transmission operation 504 may include anoperation 506 for transmitting the one or more advisories related to thehypothesis to the user as depicted in FIG. 5 a. For instance, thetransmission module 224 of the computing device 10 transmitting the oneor more advisories 90 related to the hypothesis 81* to the user 20 a.For example, transmitting to the user 20 a an advisory relating to thesoundness of the hypothesis 81* in the form of a text or audio messagesuch as “you seem to always have a stomach ache after you eat spicyfoods” or “there may be a strong link between your melancholy feelingsand cloudy weather.”

In some implementations, the transmission operation 504 may include anoperation 508 for transmitting the one or more advisories related to thehypothesis to one or more third parties as depicted in FIG. 5 a. Forinstance, the transmission module 224 of the computing device 10transmitting the one or more advisories 90 related to the hypothesis 81*to one or more third parties 50 (e.g., other users, network serviceproviders, content providers, advertisers, and so forth).

In some implementations, the advisory presentation operation 304 mayinclude a hypothesis presentation operation 510 for presenting at leastone form of the hypothesis as depicted in FIG. 5 a. For instance, thehypothesis presentation module 226 of the computing device 10 presenting(e.g., either transmitting via a network interface 120 or indicating viaa user interface 122) at least one form of the hypothesis 81* (e.g., ina graphical or iconic form, in audio form, and/or in a textual form).

In various implementations, the hypothesis presentation operation 510may include an operation 512 for presenting an indication of arelationship between at least a first event type and at least a secondevent type as referenced by the hypothesis as depicted in FIG. 5 a. Forinstance, the event types relationship presentation module 228 of thecomputing device 10 presenting an indication of a relationship (e.g.,sequential or spatial relationship) between at least a first event type(e.g., a subjective user state) and at least a second event type (e.g.,an objective occurrence) as referenced by the hypothesis 81*.

In various implementations, operation 512 may include an operation 514for presenting an indication of soundness of the hypothesis as depictedin FIG. 5 a. For instance, the soundness presentation module 230 of thecomputing device 10 presenting (e.g., either transmitting via a networkinterface 120 or indicating via a user interface 122) an indication ofsoundness of the hypothesis 81*. For example, indicating that thehypothesis 81* is a weak or a strong hypothesis.

In some implementations, operation 514 may further include an operation516 for presenting an indication of strength or weakness of correlationbetween the at least first event type and the at least second event typelinked together by the hypothesis as depicted in FIG. 5 a. For instance,the strength/weakness presentation module 232 of the computing device 10presenting (e.g., either transmitting via a network interface 120 orindicating via a user interface 122) an indication of strength orweakness of correlation between the at least first event type (e.g.,stomach ache) and the at least second event type (e.g., consuming spicyfoods) linked together by the hypothesis 81*. For example indicatingthat there is a strong or weak link between eating spicy foods andstomach ache.

In some implementations, operation 512 may include an operation 518 forpresenting an indication of a time or temporal relationship between theat least first event type and the at least second event type as depictedin FIG. 5 a. For instance, the time/temporal relationship presentationmodule 234 of the computing device 10 presenting (e.g., eithertransmitting via a network interface 120 or indicating via a userinterface 122) an indication of a time or temporal relationship betweenthe at least first event type (e.g., feeling alert) and the at leastsecond event type (e.g., exercising). For example, indicating that ifthe user 20* exercises, the user 20* may feel more alert afterwards.

In some implementations, operation 512 may include an operation 520 forpresenting an indication of a spatial relationship between the at leastfirst event type and the at least second event type as depicted in FIG.5 a. For instance, the spatial relationship presentation module 236 ofthe computing device 10 presenting (e.g., either transmitting via anetwork interface 120 or indicating via a user interface 122) anindication of a spatial relationship between the at least first eventtype (e.g., feeling relaxed) and the at least second event type (e.g.,spouse visiting a business client). For example, indicating that theuser 20* is more relaxed at home when the user's spouse is away inCalifornia on a business trip.

In various implementations, operation 512 of FIG. 5 a may include anoperation 522 for presenting an indication of a relationship between atleast a first subjective user state type and at least a secondsubjective user state type as indicated by the hypothesis as depicted inFIG. 5 b. For instance, the event types relationship presentation module228 of the computing device 10 presenting (e.g., either transmitting viaa network interface 120 or indicating via a user interface 122) anindication of a relationship (e.g., sequential relationship or spatialrelationship) between at least a first subjective user state type (e.g.,anger) and at least a second subjective user state type (e.g., mentalfatigue) as indicated by the hypothesis 81*.

In some implementations, operation 512 may include an operation 524 forpresenting an indication of a relationship between at least a firstobjective occurrence type and at least a second objective occurrencetype as indicated by the hypothesis as depicted in FIG. 5 b. Forinstance, the event types relationship presentation module 228 of thecomputing device 10 presenting (e.g., either transmitting via a networkinterface 120 or indicating via a user interface 122) an indication of arelationship (e.g., sequential relationship or spatial relationship)between at least a first objective occurrence type (e.g., consumption ofa particular medication) and at least a second objective occurrence type(e.g., elevated blood pressure) as indicated by the hypothesis 81*.

In some implementations, operation 512 may include an operation 526 forpresenting an indication of a relationship between at least a firstsubjective observation type and at least a second subjective observationtype as indicated by the hypothesis as depicted in FIG. 5 b. Forinstance, the event types relationship presentation module 228 of thecomputing device 10 presenting (e.g., either transmitting via a networkinterface 120 or indicating via a user interface 122) an indication of arelationship (e.g., sequential relationship or spatial relationship)between at least a first subjective observation type (e.g., anobservation that the workload at a place of employment appears to beheavy) and at least a second subjective observation type (e.g., anobservation that a worker appears to be very tense) as indicated by thehypothesis 81*.

In some implementations, operation 512 may include an operation 528 forpresenting an indication of a relationship between at least a subjectiveuser state type and at least an objective occurrence type as indicatedby the hypothesis as depicted in FIG. 5 b. For instance, the event typesrelationship presentation module 228 of the computing device 10presenting (e.g., either transmitting via a network interface 120 orindicating via a user interface 122) an indication of a relationship(e.g., sequential relationship or spatial relationship) between at leasta subjective user state type (e.g., anger) and at least an objectiveoccurrence type (e.g., elevated blood pressure) as indicated by thehypothesis 81*.

In some implementations, operation 512 may include an operation 530 forpresenting an indication of a relationship between at least a subjectiveuser state type and at least a subjective observation type as indicatedby the hypothesis as depicted in FIG. 5 b. For instance, the event typesrelationship presentation module 228 of the computing device 10presenting (e.g., either transmitting via a network interface 120 orindicating via a user interface 122) an indication of a relationship(e.g., sequential relationship or spatial relationship) between at leasta subjective user state type (e.g., elation) and at least a subjectiveobservation type (e.g., observation that the stock market is performingwell) as indicated by the hypothesis 81*.

In some implementations, operation 512 may include an operation 532 forpresenting an indication of a relationship between at least an objectiveoccurrence type and at least a subjective observation type as indicatedby the hypothesis as depicted in FIG. 5 b. For instance, the event typesrelationship presentation module 228 of the computing device 10presenting (e.g., either transmitting via a network interface 120 orindicating via a user interface 122) an indication of a relationship(e.g., sequential relationship or spatial relationship) between at leastan objective occurrence type (e.g., low blood pressure) and at least asubjective observation type (e.g., observation that a person appears tobe content) as indicated by the hypothesis 81*.

In various implementations, the advisory presentation operation 304 ofFIG. 3 may include an operation 534 for presenting an advisory relatingto a predication of a future event as depicted in FIG. 5 c. Forinstance, the prediction presentation module 238 of the computing device10 presenting (e.g., either transmitting via a network interface 120 orindicating via a user interface 122) an advisory relating to apredication of a future event. For example, based at least on thehypothesis 81* (e.g., a hangover linked to binge drinking) and thereporting of at least one reported event (e.g., binge drinking), anadvisory may be presented that indicates that the user 20* will have ahangover the next morning.

In various implementations, the advisory presentation operation 304 mayinclude an operation 536 for presenting a recommendation for a futurecourse of action as depicted in FIG. 5 c. For instance, therecommendation presentation module 240 of the computing device 10presenting (e.g., either transmitting via a network interface 120 orindicating via a user interface 122) a recommendation for a futureaction (e.g., “you should take a couple of aspirins this morning”).

In some implementations, operation 536 may include an operation 538 forpresenting a justification for the recommendation as depicted in FIG. 5c. For instance, the justification presentation module 242 of thecomputing device 10 presenting a justification for the recommendation(e.g., “because you consumed a lot of alcoholic beverages last night,you should take a couple of aspirins this morning”).

In some implementations, the advisory presentation operation 304 mayinclude an operation 540 for presenting an indication of one or morepast events as depicted in FIG. 5 c. For instance, the past eventspresentation module 244 of the computing device 10 presenting (e.g.,either transmitting via a network interface 120 or indicating via a userinterface 122) an indication of one or more past events (e.g., “did youknow that each time you have eaten Mexican food in the past, youdeveloped a stomach ache?”).

Those having skill in the art will recognize that the state of the arthas progressed to the point where there is little distinction leftbetween hardware and software implementations of aspects of systems; theuse of hardware or software is generally (but not always, in that incertain contexts the choice between hardware and software can becomesignificant) a design choice representing cost vs. efficiency tradeoffs.Those having skill in the art will appreciate that there are variousvehicles by which processes and/or systems and/or other technologiesdescribed herein can be effected (e.g., hardware, software, and/orfirmware), and that the preferred vehicle will vary with the context inwhich the processes and/or systems and/or other technologies aredeployed. For example, if an implementer determines that speed andaccuracy are paramount, the implementer may opt for a mainly hardwareand/or firmware vehicle; alternatively, if flexibility is paramount, theimplementer may opt for a mainly software implementation; or, yet againalternatively, the implementer may opt for some combination of hardware,software, and/or firmware. Hence, there are several possible vehicles bywhich the processes and/or devices and/or other technologies describedherein may be effected, none of which is inherently superior to theother in that any vehicle to be utilized is a choice dependent upon thecontext in which the vehicle will be deployed and the specific concerns(e.g., speed, flexibility, or predictability) of the implementer, any ofwhich may vary. Those skilled in the art will recognize that opticalaspects of implementations will typically employ optically-orientedhardware, software, and or firmware.

The foregoing detailed description has set forth various embodiments ofthe devices and/or processes via the use of block diagrams, flowcharts,and/or examples. Insofar as such block diagrams, flowcharts, and/orexamples contain one or more functions and/or operations, it will beunderstood by those within the art that each function and/or operationwithin such block diagrams, flowcharts, or examples can be implemented,individually and/or collectively, by a wide range of hardware, software,firmware, or virtually any combination thereof. In one embodiment,several portions of the subject matter described herein may beimplemented via Application Specific Integrated Circuits (ASICs), FieldProgrammable Gate Arrays (FPGAs), digital signal processors (DSPs), orother integrated formats. However, those skilled in the art willrecognize that some aspects of the embodiments disclosed herein, inwhole or in part, can be equivalently implemented in integratedcircuits, as one or more computer programs running on one or morecomputers (e.g., as one or more programs running on one or more computersystems), as one or more programs running on one or more processors(e.g., as one or more programs running on one or more microprocessors),as firmware, or as virtually any combination thereof, and that designingthe circuitry and/or writing the code for the software and or firmwarewould be well within the skill of one of skill in the art in light ofthis disclosure. In addition, those skilled in the art will appreciatethat the mechanisms of the subject matter described herein are capableof being distributed as a program product in a variety of forms, andthat an illustrative embodiment of the subject matter described hereinapplies regardless of the particular type of signal bearing medium usedto actually carry out the distribution. Examples of a signal bearingmedium include, but are not limited to, the following: a recordable typemedium such as a floppy disk, a hard disk drive, a Compact Disc (CD), aDigital Video Disk (DVD), a digital tape, a computer memory, etc.; and atransmission type medium such as a digital and/or an analogcommunication medium (e.g., a fiber optic cable, a waveguide, a wiredcommunications link, a wireless communication link, etc.).

In a general sense, those skilled in the art will recognize that thevarious aspects described herein which can be implemented, individuallyand/or collectively, by a wide range of hardware, software, firmware, orany combination thereof can be viewed as being composed of various typesof “electrical circuitry.” Consequently, as used herein “electricalcircuitry” includes, but is not limited to, electrical circuitry havingat least one discrete electrical circuit, electrical circuitry having atleast one integrated circuit, electrical circuitry having at least oneapplication specific integrated circuit, electrical circuitry forming ageneral purpose computing device configured by a computer program (e.g.,a general purpose computer configured by a computer program which atleast partially carries out processes and/or devices described herein,or a microprocessor configured by a computer program which at leastpartially carries out processes and/or devices described herein),electrical circuitry forming a memory device (e.g., forms of randomaccess memory), and/or electrical circuitry forming a communicationsdevice (e.g., a modem, communications switch, or optical-electricalequipment). Those having skill in the art will recognize that thesubject matter described herein may be implemented in an analog ordigital fashion or some combination thereof.

Those having skill in the art will recognize that it is common withinthe art to describe devices and/or processes in the fashion set forthherein, and thereafter use engineering practices to integrate suchdescribed devices and/or processes into data processing systems. Thatis, at least a portion of the devices and/or processes described hereincan be integrated into a data processing system via a reasonable amountof experimentation. Those having skill in the art will recognize that atypical data processing system generally includes one or more of asystem unit housing, a video display device, a memory such as volatileand non-volatile memory, processors such as microprocessors and digitalsignal processors, computational entities such as operating systems,drivers, graphical user interfaces, and applications programs, one ormore interaction devices, such as a touch pad or screen, and/or controlsystems including feedback loops and control motors (e.g., feedback forsensing position and/or velocity; control motors for moving and/oradjusting components and/or quantities). A typical data processingsystem may be implemented utilizing any suitable commercially availablecomponents, such as those typically found in datacomputing/communication and/or network computing/communication systems.

The herein described subject matter sometimes illustrates differentcomponents contained within, or connected with, different othercomponents. It is to be understood that such depicted architectures aremerely exemplary, and that in fact many other architectures can beimplemented which achieve the same functionality. In a conceptual sense,any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality can be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermedial components. Likewise, any two components soassociated can also be viewed as being “operably connected”, or“operably coupled”, to each other to achieve the desired functionality,and any two components capable of being so associated can also be viewedas being “operably couplable”, to each other to achieve the desiredfunctionality. Specific examples of operably couplable include but arenot limited to physically mateable and/or physically interactingcomponents and/or wirelessly interactable and/or wirelessly interactingcomponents and/or logically interacting and/or logically interactablecomponents.

While particular aspects of the present subject matter described hereinhave been shown and described, it will be apparent to those skilled inthe art that, based upon the teachings herein, changes and modificationsmay be made without departing from the subject matter described hereinand its broader aspects and, therefore, the appended claims are toencompass within their scope all such changes and modifications as arewithin the true spirit and scope of the subject matter described herein.Furthermore, it is to be understood that the invention is defined by theappended claims.

It will be understood by those within the art that, in general, termsused herein, and especially in the appended claims (e.g., bodies of theappended claims) are generally intended as “open” terms (e.g., the term“including” should be interpreted as “including but not limited to,” theterm “having” should be interpreted as “having at least,” the term“includes” should be interpreted as “includes but is not limited to,”etc.). It will be further understood by those within the art that if aspecific number of an introduced claim recitation is intended, such anintent will be explicitly recited in the claim, and in the absence ofsuch recitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to inventions containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations.

In addition, even if a specific number of an introduced claim recitationis explicitly recited, those skilled in the art will recognize that suchrecitation should typically be interpreted to mean at least the recitednumber (e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention (e.g., “a system having at least one ofA, B, and C” would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.).

In those instances where a convention analogous to “at least one of A,B, or C, etc.” is used, in general such a construction is intended inthe sense one having skill in the art would understand the convention(e.g., “a system having at least one of A, B, or C” would include butnot be limited to systems that have A alone, B alone, C alone, A and Btogether, A and C together, B and C together, and/or A, B, and Ctogether, etc.). It will be further understood by those within the artthat virtually any disjunctive word and/or phrase presenting two or morealternative terms, whether in the description, claims, or drawings,should be understood to contemplate the possibilities of including oneof the terms, either of the terms, or both terms. For example, thephrase “A or B” will be understood to include the possibilities of “A”or “B” or “A and B.”

1. A system in the form of a machine, an article of manufacture, or acomposition of matter, comprising: a hypothesis selection moduleconfigured to select a hypothesis from a plurality of hypotheses, theselection of the hypothesis being based, at least in part, on at leastone reported event associated with a user, at least the hypothesis to beselected being relevant to the user, wherein said hypothesis selectionmodule including a comparison module, the hypothesis selection moduleconfigured to select the hypothesis based, at least in part, on acomparison as made by the comparison module of the at least one reportedevent to one, or both, of a first event type and a second event typelinked together by the hypothesis; wherein said hypothesis selectionmodule is configured to select the hypothesis based, at least in part,on the comparison module comparing a second reported event to one, orboth, of the first event type and the second event type; an advisorypresentation module configured to present at least one advisory relatedto the selected hypothesis; and a memory.
 2. The system of claim 1,wherein said hypothesis selection module is configured to select thehypothesis by selecting a hypothesis relating to at least one of asubjective mental state type, a subjective physical state type, or asubjective overall state type.
 3. The system of claim 1, wherein saidhypothesis selection module is configured to select the hypothesis byselecting a hypothesis relating to at least one or more objectiveoccurrence types.
 4. The system of claim 3, wherein said hypothesisselection module is configured to select the hypothesis by selecting ahypothesis relating to one or more types of user activities.
 5. Thesystem of claim 4, wherein said hypothesis selection module isconfigured to select the hypothesis by selecting a hypothesis relatingto a consumption of a type of food item, a type of medicine, or a typeof nutraceutical.
 6. The system of claim 4, wherein said hypothesisselection module is configured to select the hypothesis by selecting ahypothesis relating to at least a type of exercise activity.
 7. Thesystem of claim 4, wherein said hypothesis selection module isconfigured to select the hypothesis by selecting a hypothesis relatingto at least a type of social activity.
 8. The system of claim 4, whereinsaid hypothesis selection module is configured to select the hypothesisby selecting a hypothesis relating to at least a type of recreationalactivity.
 9. The system of claim 4, wherein said hypothesis selectionmodule is configured to select the hypothesis by selecting a hypothesisrelating to at least a type of learning or type of educational activity.10. The system of claim 3, wherein said hypothesis selection module isconfigured to select the hypothesis by selecting a hypothesis relatingto at least one type of activity performed by at least one third party.11. The system of claim 3, wherein said hypothesis selection module isconfigured to select the hypothesis by selecting a hypothesis relatingto at least one type of user physical characteristic.
 12. The system ofclaim 3, wherein said hypothesis selection module is configured toselect the hypothesis by selecting a hypothesis relating to at least onetype of external event.
 13. The system of claim 3, wherein saidhypothesis selection module is configured to select the hypothesis byselecting a hypothesis relating to at least one location.
 14. The systemof claim 1, wherein said hypothesis selection module is configured toselect the hypothesis by selecting a hypothesis relating to one or moresubjective observation types.
 15. The system of claim 14, wherein saidhypothesis selection module is configured to select the hypothesis byselecting a hypothesis relating to at least one type of subjectiveobservation related to a person.
 16. The system of claim 15, whereinsaid hypothesis selection module is configured to select the hypothesisby selecting a hypothesis relating to at least one type of subjectiveobservation of at least a subjective mental state, a subjective physicalstate, or a subjective overall state of the person.
 17. The system ofclaim 14, wherein said hypothesis selection module is configured toselect the hypothesis by selecting a hypothesis relating to at least onetype of subjective observation related to at least a type of activityperformed by a person.
 18. The system of claim 14, wherein saidhypothesis selection module is configured to select the hypothesis byselecting a hypothesis relating to at least one type of subjectiveobservation related to an occurrence of an external event.
 19. Thesystem of claim 1, wherein said hypothesis selection module isconfigured to select the hypothesis by selecting a hypothesis linking atleast a first event type with at least a second event type.
 20. Thesystem of claim 19, wherein said hypothesis selection module isconfigured to select the hypothesis from the plurality of hypotheses,the hypothesis to be selected linking at least a first subjective userstate type with at least a second subjective user state type.
 21. Thesystem of claim 19, wherein said hypothesis selection module: isconfigured to select the hypothesis from the plurality of hypotheses,the hypothesis to be selected linking at least one subjective user statetype with at least one objective occurrence type.
 22. The system ofclaim 19, wherein said hypothesis selection module is configured toselect the hypothesis from the plurality of hypotheses, the hypothesisto be selected linking at least one subjective user state type with atleast one subjective observation type.
 23. The system of claim 19,wherein said hypothesis selection module is configured to select thehypothesis from the plurality of hypotheses, the hypothesis to beselected linking at least a first objective occurrence type with atleast a second objective occurrence type.
 24. The system of claim 19,wherein said hypothesis selection module is configured to select thehypothesis from the plurality of hypotheses, the hypothesis to beselected linking at least one objective occurrence type with at leastone subjective observation type.
 25. The system of claim 19, whereinsaid hypothesis selection module is configured to select the hypothesisfrom the plurality of hypotheses, the hypothesis to be selected linkingat least a first subjective observation type with at least a secondsubjective observation type.
 26. The system of claim 1, wherein saidhypothesis selection module is configured to select the hypothesis froma plurality of hypotheses that are specifically associated with theuser.
 27. The system of claim 1, wherein said hypothesis selectionmodule is configured to select the hypothesis from a plurality ofhypotheses that are specifically associated with at least a sub-group ofa population, the user being a member of the sub-group.
 28. The systemof claim 1, wherein said hypothesis selection module is configured toselect the hypothesis based, at least in part, on at least one reportedevent reported via one or more electronic entries received by areception module.
 29. The system of claim 28, wherein said hypothesisselection module is configured to select the hypothesis based, at leastin part, on at least one reported event reported via one or more blogentries received by the reception module.
 30. The system of claim 28,wherein said hypothesis selection module is configured to select thehypothesis based, at least in part, on at least one reported eventreported via one or more status reports received by the receptionmodule.
 31. The system of claim 28, wherein said hypothesis selectionmodule is configured to select the hypothesis based, at least in part,on at least one reported event reported via one or more electronicentries composed by the user and received by the reception module. 32.The system of claim 28, wherein said hypothesis selection module isconfigured to select the hypothesis based, at least in part, on at leastone reported event reported via one or more electronic entries composedby one or more third parties and received by the reception module. 33.The system of claim 28, wherein said hypothesis selection module isconfigured to select the hypothesis based, at least in part, on at leastone reported event reported via one or more electronic entries generatedby one or more remote network devices and received by the receptionmodule.
 34. The system of claim 33, wherein said hypothesis selectionmodule is configured to select the hypothesis based, at least in part,on at least one reported event reported via one or more electronicentries generated by one or more sensors and received by the receptionmodule.
 35. The system of claim 1, wherein said hypothesis selectionmodule is configured to select the hypothesis based, at least in part,on at least the one reported event and a second reported event.
 36. Thesystem of claim 35, wherein said hypothesis selection module isconfigured to select the hypothesis based, at least in part, on at leastone reported event that originates from a first source and a secondreported event that originates from a second source.
 37. The system ofclaim 1, wherein said hypothesis selection module including arelationship determination module, the hypothesis selection moduleconfigured to select the hypothesis based, at least in part, on therelationship determination module determining a relationship between thefirst reported event and the second reported event.
 38. The system ofclaim 37, wherein said hypothesis selection module including therelationship determination module and a sequential link determinationmodule, the hypothesis selection module configured to select thehypothesis based, at least in part, on the sequential link determinationmodule determining a sequential link between the first reported eventand the second reported event.
 39. The system of claim 37, wherein saidhypothesis selection module including the relationship determinationmodule and a spatial link determination module, the hypothesis selectionmodule configured to select the hypothesis based, at least in part, onthe spatial link determination module determining a spatial link betweenthe first reported event and the second reported event.
 40. The systemof claim 37, wherein said hypothesis selection module configured toselect the hypothesis based, at least in part, on the comparison modulecomparing the relationship between the first reported event and thesecond reported event to a relationship between the first event type andthe second event type of the hypothesis.
 41. A system, comprising:circuitry for selecting a hypothesis from a plurality of hypotheses, theselection of the hypothesis being based, at least in part, on at leastone reported event associated with a user, at least the hypothesis to beselected being relevant to the user; wherein said circuitry forselecting the hypothesis from the plurality of hypotheses includescircuitry for selecting the hypothesis from the plurality of hypothesesbased, at least in part, on a comparison of the at least one reportedevent to one, or both, of a first event type and a second event typelinked together by the hypothesis; wherein said circuitry for selectingthe hypothesis from the plurality of hypothesis includes circuitry forselecting the hypothesis from the plurality of hypotheses based, atleast in part, on comparing a second reported event to one, or both, ofthe first event type and the second event type; and circuitry forpresenting one or more advisories related to the hypothesis.
 42. Anarticle of manufacture, comprising: a non-transitory storage mediumbearing: one or more instructions for selecting a hypothesis from aplurality of hypotheses, the selection of the hypothesis being based, atleast in part, on at least one reported event associated with a user, atleast the hypothesis to be selected being relevant to the user; whereinsaid one or more instructions for selecting the hypothesis from theplurality of hypotheses includes one or more instructions for selectingthe hypothesis from the plurality of hypotheses based, at least in part,on a comparison of the at least one reported event to one, or both, of afirst event type and a second event type linked together by thehypothesis; wherein said one or more instructions for selecting thehypothesis from the plurality of hypotheses includes one or moreinstructions for selecting the hypothesis from the plurality ofhypotheses based, at least in part, on comparing a second reported eventto one, or both, of the first event type and the second event type; andone or more instructions for presenting one or more advisories relatedto the hypothesis.